🔬Modal Research Exchange

{
  "activation_gate": {
    "applicable": true,
    "checks": [
      {
        "measures": "model name passed to create_lora_training_client in every arm",
        "metric": "executed_base_model",
        "one_of": [
          "Qwen/Qwen3.5-2B"
        ]
      },
      {
        "measures": "type of params object passed to optim_step in each arm (logged client-side)",
        "metric": "executed_optimizer_step_type",
        "one_of": [
          "DimuonParams",
          "AdamParams"
        ]
      },
      {
        "max": 1.05,
        "measures": "||dB||_F^2 / (||dB||_2^2 * r) for the first DiMuon update of LoRA B, averaged over layers, from downloaded checkpoints at step 0 and 1",
        "metric": "dimuon_B_update_normalized_stable_rank_step1",
        "min": 0.6
      },
      {
        "max": 1.0,
        "measures": "fraction of the 100 training batches whose token-id hash is identical between paired arms within a seed",
        "metric": "batch_hash_match_across_arms",
        "min": 1.0
      },
      {
        "measures": "train token_mean NLL at step 0 minus step 10 in the DiMuon pilot at the middle LR; must be finite and positive",
        "metric": "pilot_train_nll_drop_dimuon_10steps",
        "min": 0.0
      }
    ],
    "reason": "The paired comparison only tests DiMuon if the executed optimizer really orthogonalizes LoRA B updates and both arms ran the declared configuration. Polar Express makes the first-step B update near-orthogonal (flat singular spectrum); AdamW's is not. This is measured from saved weight diffs, not SDK introspection."
  },
  "arms": [
    {
      "lr": 5e-05,
      "name": "lrsearch_adamw_5e-5_s0",
      "optimizer": "adam",
      "phase": "selection",
      "seed": 0,
      "steps": 40
    },
    {
      "lr": 0.0001,
      "name": "lrsearch_adamw_1e-4_s0",
      "optimizer": "adam",
      "phase": "selection",
      "seed": 0,
      "steps": 40
    },
    {
      "lr": 0.0002,
      "name": "lrsearch_adamw_2e-4_s0",
      "optimizer": "adam",
      "phase": "selection",
      "seed": 0,
      "steps": 40
    },
    {
      "lr": 0.0003,
      "name": "lrsearch_dimuon_3e-4_s0",
      "optimizer": "dimuon-lite",
      "phase": "selection",
      "seed": 0,
      "steps": 40
    },
    {
      "lr": 0.001,
      "name": "lrsearch_dimuon_1e-3_s0",
      "optimizer": "dimuon-lite",
      "phase": "selection",
      "seed": 0,
      "steps": 40
    },
    {
      "lr": 0.003,
      "name": "lrsearch_dimuon_3e-3_s0",
      "optimizer": "dimuon-lite",
      "phase": "selection",
      "seed": 0,
      "steps": 40
    },
    {
      "lr": "frozen from selection",
      "name": "baseline_adamw_s0",
      "optimizer": "adam",
      "phase": "confirmation",
      "seed": 0,
      "steps": 100
    },
    {
      "lr": "frozen from selection",
      "name": "baseline_adamw_s1",
      "optimizer": "adam",
      "phase": "confirmation",
      "seed": 1,
      "steps": 100
    },
    {
      "lr": "frozen from selection",
      "name": "baseline_adamw_s2",
      "optimizer": "adam",
      "phase": "confirmation",
      "seed": 2,
      "steps": 100
    },
    {
      "lr": "frozen from selection",
      "name": "treatment_dimuon_s0",
      "optimizer": "dimuon-lite",
      "phase": "confirmation",
      "seed": 0,
      "steps": 100
    },
    {
      "lr": "frozen from selection",
      "name": "treatment_dimuon_s1",
      "optimizer": "dimuon-lite",
      "phase": "confirmation",
      "seed": 1,
      "steps": 100
    },
    {
      "lr": "frozen from selection",
      "name": "treatment_dimuon_s2",
      "optimizer": "dimuon-lite",
      "phase": "confirmation",
      "seed": 2,
      "steps": 100
    }
  ],
  "base_model": "Qwen/Qwen3.5-2B",
  "baseline": "AdamW (tinker.AdamParams, beta1 0.9, beta2 0.95, eps 1e-8) through the same SDK script, with the same model, LoRA rank, init seed, batches and order, 100 steps, and eval. Its LR is chosen by the same 3-point validation search.",
  "batch_size": 32,
  "budget": {
    "gpu_hours": 2.0,
    "max_wall_minutes": 150
  },
  "command": "# Custom part: one SDK SFT loop shared by both optimizers (cookbook chat_sl is AdamW-only).\nimport os, sys, tinker, datasets, random\nfrom tinker_cookbook import renderers, tokenizer_utils\nfrom tinker_cookbook.supervised.data import conversation_to_datum\nOPT, LR, SEED, STEPS = sys.argv[1], float(sys.argv[2]), int(sys.argv[3]), int(sys.argv[4])\nMODEL='Qwen/Qwen3.5-2B'; BS=32; MAXLEN=2048\ntok=tokenizer_utils.get_tokenizer(MODEL); rend=renderers.get_renderer(renderers.get_recommended_renderer_name(MODEL) if hasattr(renderers,'get_recommended_renderer_name') else 'qwen3', tok)\nds=datasets.load_dataset('HuggingFaceH4/no_robots'); tr=list(ds['train']); random.Random(1234).shuffle(tr)\nval, train = tr[:500], tr[500:]; random.Random(SEED).shuffle(train)\nmk=lambda r: conversation_to_datum(r['messages'], rend, MAXLEN)\nsc=tinker.ServiceClient(base_url=os.environ['TINKER_BASE_URL'])\nkw={'optimizer': tinker.types.DimuonOptimizerConfig()} if OPT=='dimuon' else {}\ntc=sc.create_lora_training_client(base_model=MODEL, rank=32, seed=SEED, **kw)\ntc.save_weights_for_sampler(name=f'init-{OPT}-s{SEED}').result()  # hash for restoration preflight\nstep_params = (lambda: tinker.types.DimuonParams(learning_rate=LR, beta1=0.9, grad_clip_norm=0.0)) if OPT=='dimuon' else (lambda: tinker.AdamParams(learning_rate=LR, beta1=0.9, beta2=0.95, eps=1e-8))\nfor s in range(STEPS):\n    batch=[mk(r) for r in train[s*BS:(s+1)*BS]]\n    tc.forward_backward(batch, loss_fn='cross_entropy').result(); tc.optim_step(step_params()).result()\ndef nll(rows):  # token_mean from per-example totals, never batch loss:mean\n    tot=0.0; n=0.0\n    for i in range(0,len(rows),32):\n        d=[mk(r) for r in rows[i:i+32]]; out=tc.forward(d, loss_fn='cross_entropy').result()\n        for di,o in zip(d,out.loss_fn_outputs):\n            w=di.loss_fn_inputs['weights'].to_numpy(); lp=o['logprobs'].to_numpy(); tot+=-(lp*w).sum(); n+=w.sum()\n    return tot/n\nsplit = val if STEPS==40 else list(ds['test'])\nprint({'opt':OPT,'lr':LR,'seed':SEED,'steps':STEPS,'token_mean_nll':nll(split)})\ntc.save_weights_for_sampler(name=f'final-{OPT}-lr{LR}-s{SEED}').result()\n# run: python sft_opt.py {adam|dimuon} <lr> <seed> {40|100}",
  "correctness_preflight": {
    "notes": "Tolerances are fixed for bf16 LoRA weights with fp32 optimizer math before measuring. reference_update rtol 1e-2 leaves headroom over the reported 0.09% error. Feed the same LoRA state and gradient to the Spindle DiMuon step and to the reference for one step. With grad_clip_norm=0 and beta1 momentum seeded from the first gradient, the update should scale linearly in LR across a 100x span.",
    "paths": [
      {
        "baseline_revision": "UNRESOLVED: pin the modal-projects/spindle commit deployed at the shared Spindle URL (post-#85). AdamW updates on this deployment must match the pre-#85 AdamW update on identical grads/state.",
        "id": "spindle_dimuon_lite_optimizer",
        "kind": "optimizer",
        "lr_scaling": {
          "learning_rates": [
            0.0001,
            0.001,
            0.01
          ],
          "rtol": 0.01
        },
        "reference_revision": "UNRESOLVED: pin the commit of Aaron's DiMuon reconstruction (0.09% relative update error vs Tinker) and the modal-projects/miles commit of #17",
        "tolerances": {
          "baseline_update": {
            "atol": 1e-06,
            "min_cosine": 0.99999,
            "rtol": 0.001
          },
          "reference_update": {
            "atol": 1e-05,
            "min_cosine": 0.999,
            "rtol": 0.01
          }
        }
      }
    ],
    "required": true,
    "status": "unresolved: spindle and miles commits for #85/#17 and the commit of Aaron's DiMuon reconstruction must be pinned before any full sweep"
  },
  "dataset": {
    "hf_path": "HuggingFaceH4/no_robots",
    "n_examples": 9500,
    "name": "no_robots",
    "split": "train (9000 for training, 500 held out for LR selection); test (500) for confirmation"
  },
  "decision_rule": {
    "adaptive_selection": {
      "confirmation_item_ids": "all 500 rows of no_robots split=test",
      "kind": "hyperparameter",
      "rationale": "Muon-family and AdamW LoRA optimizers have very different good LRs. Each gets a prespecified 3-point grid, scored only on validation items held out from train, and the choice is frozen before the single confirmation eval on the test split.",
      "search_item_ids": "no_robots train rows held out by seeded shuffle (seed 1234), first 500 after shuffle; never used for training"
    },
    "alpha": 0.05,
    "assumptions": {
      "identically_distributed_seed_effects": true,
      "independent_seed_effects": true,
      "normal_seed_effects": true
    },
    "criteria": [
      {
        "id": "primary",
        "margin": 0.02,
        "metric": "no_robots_test_token_mean_nll_at_step_100",
        "weights": {
          "baseline": 1,
          "treatment": -1
        }
      }
    ],
    "estimand": "Reduction in held-out no_robots test token_mean NLL at step 100 from replacing AdamW with dimuon-lite (each at its validation-selected LR) for rank-32 LoRA SFT on Qwen3.5-2B; larger is better for dimuon-lite.",
    "method": "paired_t",
    "seeds": [
      0,
      1,
      2
    ],
    "unit": "training_seed",
    "version": 1
  },
  "eval": {
    "aggregation": "token_mean",
    "benchmark": "no_robots test split (HuggingFaceH4/no_robots, split=test)",
    "metric": "held-out token_mean NLL (nats per assistant loss token)",
    "n_examples": 500
  },
  "experiment_features": {
    "adaptive_selection": true,
    "checkpoint_restore": false,
    "custom_loss": false,
    "custom_optimizer": true,
    "generated_training_data": false,
    "heldout_loss": true,
    "learned_generator": false,
    "paired_comparison": true,
    "paired_training_arms": true,
    "warmup_adaptation": false
  },
  "feasibility": {
    "config": null,
    "downsizing_order": "drop IFEval secondary, then shrink LR grid to 2 points per optimizer, then shorten selection runs to 25 steps; keep 3 seeds and the 100-step test NLL. If that still does not fit, report inconclusive.",
    "effective_config": {
      "arms": 12,
      "base_model": "Qwen/Qwen3.5-2B",
      "batch_size": 32,
      "confirmation_steps": 100,
      "max_seq_len": 2048,
      "n_eval_test": 500,
      "n_eval_validation": 500,
      "n_ifeval_secondary": 541,
      "planned_arm_steps": 840,
      "selection_steps": 40
    },
    "generation": null,
    "measurements": null,
    "primary_endpoint": "no_robots_test_token_mean_nll_at_step_100",
    "recovery": null,
    "source": null
  },
  "feasibility_check": {
    "generation_applicability": {
      "check": "generation_audit",
      "enforce": false,
      "features": {
        "generated_training_data": false
      },
      "reasons": [
        "spec.generation_audit opts out and no feature it covers is true"
      ],
      "status": "not_applicable"
    },
    "generation_counts": null,
    "gpu_hours": null,
    "known_gpu_hours": 0,
    "phases": {},
    "problems": [
      "Effective configuration not recorded",
      "Pilot evidence source not recorded",
      "Representative training timing not recorded",
      "Representative setup timing not recorded",
      "Representative evaluation timing not recorded",
      "Positive recovery reserve not recorded"
    ],
    "status": "unknown",
    "wall_seconds": null
  },
  "feasibility_required": true,
  "generation_audit": {
    "answer_comparison": null,
    "max_eval_overlaps": 0,
    "max_label_errors": 0,
    "min_sample_size": 0,
    "reason": "fixed public dataset; no generated training data",
    "required": false
  },
  "hypothesis": "With per-optimizer learning rates picked on a validation split, LoRA SFT with dimuon-lite cuts held-out no_robots token_mean NLL by at least 0.02 nats versus AdamW. Both arms use the same 100 steps, batches and seeds.",
  "lora_rank": 32,
  "max_seq_len": 2048,
  "method": "Train a rank-32 LoRA on Qwen/Qwen3.5-2B over HuggingFaceH4/no_robots, using one tinker SDK script for both arms so the only difference is the optimizer. The cookbook chat_sl recipe is AdamW-only, so running AdamW through the same script removes any recipe difference. DiMuon is selected at creation with optimizer=tinker.types.DimuonOptimizerConfig() and stepped with DimuonParams(learning_rate, beta1=0.9, grad_clip_norm=0.0). AdamW uses tinker.AdamParams(learning_rate, beta1=0.9, beta2=0.95, eps=1e-8). dimuon-lite is a recently landed reverse-engineered update rule: Polar Express orthogonalization with about 5 iterations, rank_lr_correction_exponent 0.175, Muon on LoRA B, and no beta2. Its update is checked against the trusted DiMuon reconstruction before the comparison runs. Phase 1, LR selection: each optimizer runs 40 steps at seed 0 on a 3-point LR grid fixed now (AdamW {5e-5, 1e-4, 2e-4}; DiMuon {3e-4, 1e-3, 3e-3}). Each run is scored by token_mean NLL on 500 validation items held out from the train split, and the best LR per optimizer is frozen. Phase 2, confirmation: each optimizer trains 100 steps at its frozen LR for seeds {0,1,2}. Batches are identical across arms within a seed: the same shuffled order of the remaining 9,000 train rows, batch_size 32, max_seq_len 2048. Data order and LoRA init seed are shared per seed. Primary eval is token_mean NLL on the full 500-example no_robots test split, computed from per-example summed logprobs times loss weights via forward(). Secondary, descriptive only: IFEval prompt-level strict accuracy (541 prompts, greedy, max_tokens 1024) on each final checkpoint. IFEval is dropped first if feasibility does not fit.",
  "optimizer": {
    "lr": 0.001,
    "name": "dimuon-lite"
  },
  "primary_endpoint": "no_robots_test_token_mean_nll_at_step_100",
  "restoration_preflight": {
    "applicable": true,
    "fixed_batch_required": true,
    "groups": [
      {
        "arms": [
          {
            "name": "baseline_adamw_s0",
            "optimizer": "adam",
            "optimizer_config": {
              "beta1": 0.9,
              "beta2": 0.95,
              "eps": 1e-08,
              "lr": "frozen"
            }
          },
          {
            "name": "treatment_dimuon_s0",
            "optimizer": "dimuon-lite",
            "optimizer_config": {
              "beta1": 0.9,
              "grad_clip_norm": 0.0,
              "lr": "frozen"
            }
          }
        ]
      },
      {
        "arms": [
          {
            "name": "baseline_adamw_s1",
            "optimizer": "adam",
            "optimizer_config": {
              "beta1": 0.9,
              "beta2": 0.95,
              "eps": 1e-08,
              "lr": "frozen"
            }
          },
          {
            "name": "treatment_dimuon_s1",
            "optimizer": "dimuon-lite",
            "optimizer_config": {
              "beta1": 0.9,
              "grad_clip_norm": 0.0,
              "lr": "frozen"
            }
          }
        ]
      },
      {
        "arms": [
          {
            "name": "baseline_adamw_s2",
            "optimizer": "adam",
            "optimizer_config": {
              "beta1": 0.9,
              "beta2": 0.95,
              "eps": 1e-08,
              "lr": "frozen"
            }
          },
          {
            "name": "treatment_dimuon_s2",
            "optimizer": "dimuon-lite",
            "optimizer_config": {
              "beta1": 0.9,
              "grad_clip_norm": 0.0,
              "lr": "frozen"
            }
          }
        ]
      }
    ],
    "reason": "AdamW and DiMuon arms are trained from a shared fresh-LoRA start within each seed. Before the first update, each arm calls save_weights and downloads the adapter. The SHA-256 of the LoRA tensors must match across the paired arms; B=0 at init, and A comes from the seeded init. Base-model logprobs on a fixed tokenized batch must also match. A hash mismatch makes that seed inconclusive. Optimizer type is verified by the activation-gate step-1 spectrum check, not by invented introspection."
  },
  "steps": 100,
  "success_criterion": "Validated if the simultaneous 95% paired_t interval, across 3 training seeds, for (AdamW NLL \u2212 DiMuon NLL) on no_robots test token_mean NLL at step 100 lies entirely above 0.02 nats. Failed if it lies entirely below 0.02. Otherwise inconclusive. IFEval strict accuracy is reported descriptively and does not decide.",
  "title": "dimuon-lite vs AdamW, LoRA SFT on Qwen3.5-2B, no_robots held-out NLL",
  "training_type": "sft"
}

[
  {
    "arm": "baseline_adamw_s0",
    "step": 100,
    "uri": "tinker://b61dfd929fd09745b512c7224450cbde/weights/confirm-baseline_adamw_s0-final"
  },
  {
    "arm": "treatment_dimuon_s0",
    "step": 100,
    "uri": "tinker://d331297013cae43230697585afa80dc5/weights/confirm-treatment_dimuon_s0-final"
  },
  {
    "arm": "baseline_adamw_s1",
    "step": 100,
    "uri": "tinker://9c18b804f21a06705c6da69577f80b4e/weights/confirm-baseline_adamw_s1-final"
  },
  {
    "arm": "treatment_dimuon_s1",
    "step": 100,
    "uri": "tinker://64eea06e33ba66329c486ce2de950f32/weights/confirm-treatment_dimuon_s1-final"
  },
  {
    "arm": "baseline_adamw_s2",
    "step": 100,
    "uri": "tinker://581c1850d38ffa76474e2c6c7f2a3fb1/weights/confirm-baseline_adamw_s2-final"
  },
  {
    "arm": "treatment_dimuon_s2",
    "step": 100,
    "uri": "tinker://8e9fef4c99e9c1af8d68a14c2c921011/weights/confirm-treatment_dimuon_s2-final"
  },
  {
    "arm": "sel_adamw_lr5e-05_s0",
    "step": 40,
    "uri": "tinker://f23fad8a5cc3c0a7883ead5d7fb0188c/weights/select-sel_adamw_lr5e-05_s0-final"
  },
  {
    "arm": "sel_adamw_lr0.0001_s0",
    "step": 40,
    "uri": "tinker://34671def1a8e798b05dd051092ac4307/weights/select-sel_adamw_lr0.0001_s0-final"
  },
  {
    "arm": "sel_adamw_lr0.0002_s0",
    "step": 40,
    "uri": "tinker://99ddc7356a2150f7dbafc5262693978b/weights/select-sel_adamw_lr0.0002_s0-final"
  },
  {
    "arm": "sel_dimuon_lr0.0003_s0",
    "step": 40,
    "uri": "tinker://3f4f84e3c9c6567b09cde46deae56415/weights/select-sel_dimuon_lr0.0003_s0-final"
  },
  {
    "arm": "sel_dimuon_lr0.001_s0",
    "step": 40,
    "uri": "tinker://4d27e2e59c33df3015a266e8878b457d/weights/select-sel_dimuon_lr0.001_s0-final"
  },
  {
    "arm": "sel_dimuon_lr0.003_s0",
    "step": 40,
    "uri": "tinker://38efef6933464782c8204a61c7fec196/weights/select-sel_dimuon_lr0.003_s0-final"
  }
]

{
  "baseline_adamw_s0": {
    "lr": 0.0002,
    "test_example_mean_nll_step100": 1.911837178105109,
    "test_token_mean_nll_step100": 2.0330268793696935
  },
  "baseline_adamw_s1": {
    "lr": 0.0002,
    "test_example_mean_nll_step100": 1.9122966738185496,
    "test_token_mean_nll_step100": 2.0315072699517907
  },
  "baseline_adamw_s2": {
    "lr": 0.0002,
    "test_example_mean_nll_step100": 1.9098644363144257,
    "test_token_mean_nll_step100": 2.030763545006581
  },
  "sel_adamw_lr0.0001_s0": {
    "val_token_mean_nll_step40": 2.0661315833516616
  },
  "sel_adamw_lr0.0002_s0": {
    "val_token_mean_nll_step40": 2.063218484765968
  },
  "sel_adamw_lr5e-05_s0": {
    "val_token_mean_nll_step40": 2.0706255712024144
  },
  "sel_dimuon_lr0.0003_s0": {
    "val_token_mean_nll_step40": 2.250428290149688
  },
  "sel_dimuon_lr0.001_s0": {
    "val_token_mean_nll_step40": 2.1062885413010375
  },
  "sel_dimuon_lr0.003_s0": {
    "val_token_mean_nll_step40": 2.0673232168471443
  },
  "treatment_dimuon_s0": {
    "lr": 0.003,
    "test_example_mean_nll_step100": 1.9111400255469455,
    "test_token_mean_nll_step100": 2.0323170273395057
  },
  "treatment_dimuon_s1": {
    "lr": 0.003,
    "test_example_mean_nll_step100": 1.909386036657386,
    "test_token_mean_nll_step100": 2.0304049240877835
  },
  "treatment_dimuon_s2": {
    "lr": 0.003,
    "test_example_mean_nll_step100": 1.9099000514950917,
    "test_token_mean_nll_step100": 2.030674625589407
  }
}

primary_mean_adamw_minus_dimuon

0.0006337

primary_ci95_low

-0.0006357

primary_ci95_high

0.001903

primary_sd

0.000511

margin

0.02

diff_adamw_minus_dimuon_s0

0.0007099

diff_adamw_minus_dimuon_s1

0.001102

diff_adamw_minus_dimuon_s2

8.892e-05

selected_lr_adamw

0.0002

selected_lr_dimuon

0.003

paired_batch_hash_match_confirmation

1

wall_minutes

68.38

trainer_gpu_hours

1.02

• checkpoint · baseline_adamw_s0 · step 100 · tinker://b61dfd929fd09745b512c7224450cbde/weights/confirm-baseline_adamw_s0-final
• checkpoint · treatment_dimuon_s0 · step 100 · tinker://d331297013cae43230697585afa80dc5/weights/confirm-treatment_dimuon_s0-final
• checkpoint · baseline_adamw_s1 · step 100 · tinker://9c18b804f21a06705c6da69577f80b4e/weights/confirm-baseline_adamw_s1-final
• checkpoint · treatment_dimuon_s1 · step 100 · tinker://64eea06e33ba66329c486ce2de950f32/weights/confirm-treatment_dimuon_s1-final
• checkpoint · baseline_adamw_s2 · step 100 · tinker://581c1850d38ffa76474e2c6c7f2a3fb1/weights/confirm-baseline_adamw_s2-final
• checkpoint · treatment_dimuon_s2 · step 100 · tinker://8e9fef4c99e9c1af8d68a14c2c921011/weights/confirm-treatment_dimuon_s2-final
• checkpoint · sel_adamw_lr5e-05_s0 · step 40 · tinker://f23fad8a5cc3c0a7883ead5d7fb0188c/weights/select-sel_adamw_lr5e-05_s0-final
• checkpoint · sel_adamw_lr0.0001_s0 · step 40 · tinker://34671def1a8e798b05dd051092ac4307/weights/select-sel_adamw_lr0.0001_s0-final
• checkpoint · sel_adamw_lr0.0002_s0 · step 40 · tinker://99ddc7356a2150f7dbafc5262693978b/weights/select-sel_adamw_lr0.0002_s0-final
• checkpoint · sel_dimuon_lr0.0003_s0 · step 40 · tinker://3f4f84e3c9c6567b09cde46deae56415/weights/select-sel_dimuon_lr0.0003_s0-final
• checkpoint · sel_dimuon_lr0.001_s0 · step 40 · tinker://4d27e2e59c33df3015a266e8878b457d/weights/select-sel_dimuon_lr0.001_s0-final
• checkpoint · sel_dimuon_lr0.003_s0 · step 40 · tinker://38efef6933464782c8204a61c7fec196/weights/select-sel_dimuon_lr0.003_s0-final
• script · run.py sha256 8480b967ec29
• script · common.py sha256 e4258da51051
• script · fork_config.py sha256 83939e181c12
• script · preflight.py sha256 466afac60991
• script · analyze_preflight.py sha256 4574d9d3d7c5
• script · analyze2.py sha256 abf7fc94170c
• script · local_parity.py sha256 9c79b2ed02ff
• script · build_preflight.py sha256 c74c0017b314
• script · launch_confirm.py sha256 0b448f42bedd
• script · final_analysis.py sha256 a0eb6a3e00fe
• script · restore_check.py sha256 995a05bc7fcf
• script · build_output.py sha256 137937f13a7d
• script · smoke.py sha256 c3c36f5a2e18
• script · probe_shared.py sha256 20499f5b52d3
• instructions · launch-instructions.md sha256 8e52fc8e31fe
• effective_config · effective-config.json sha256 1d9e8504b61c
• code_revision · code-revision.txt sha256 561d1418f25e
• code_patch · spindle_fork.patch sha256 e99d856d7c3d
• script · dimuon_candidate.py sha256 31c5f60cb273
• predictions · predictions-confirm-test.jsonl sha256 12d303555ef2
• metrics · scored-observations-final.jsonl sha256 620c1d8cea25
• metrics · select_log.jsonl sha256 30fde247db6d
• metrics · confirm_log.jsonl sha256 6e81489e1ae7
• metrics · pilot_log.jsonl sha256 76c36589a1f8
• metrics · correctness_preflight.json sha256 c644004dfc54
• metrics · preflight_results.json sha256 a81ba4bccec2
• metrics · local_parity.json sha256 b86d75974be2
• metrics · restore_check.json sha256 94bb1e212364
• metrics · selection_frozen.json sha256 757269f012e6
• metrics · final_numbers.json sha256 3d1e2943d353
• metrics · cleanup.json sha256 64945a4c8629

1. Nov 3, 22:42 UTC · succeeded · done: sel_adamw_lr5e-05_s0, sel_adamw_lr0.0001_s0, sel_adamw_lr0.0002_s0, sel_dimuon_lr0.0003_s0, sel_dimuon_lr0.001_s0, sel_dimuon_lr0.003_s0, baseline_adamw_s0, treatment_dimuon_s0, baseline_adamw_s1, treatment_dimuon_s1, baseline_adamw_s2, treatment_dimuon_s2 · On fork spindle-fork-run_739abc9a8b, 100-step rank-32 LoRA SFT of Qwen3.5-2B on no_robots gave AdamW-minus-Dimuon test token-mean NLL +0.0006 nats (95% paired-t CI [-0.0006, +0.0019], 3 seeds) at sel…
2. Nov 3, 22:21 UTC · running · done: sel_adamw_lr5e-05_s0, sel_adamw_lr0.0001_s0, sel_adamw_lr0.0002_s0, sel_dimuon_lr0.0003_s0, sel_dimuon_lr0.001_s0, sel_dimuon_lr0.003_s0 · Correctness preflight and activation gate passed on fork spindle-fork-run_739abc9a8b. LR selection (40 steps, 500 val items) picked AdamW 2e-4 and Dimuon 3e-3, both at the upper grid edge (makes the …
3. Nov 3, 22:09 UTC · running · Fork spindle-fork-run_739abc9a8b deployed (Spindle 5148cb6 + proxy-auth patch, Miles 228b59d). Correctness preflight passed research_exchange.correctness; activation gate passed; LR selection (6 arms…
4. Nov 3, 22:04 UTC · running · Fork spindle-fork-run_739abc9a8b deployed (Spindle 5148cb6 + proxy-auth patch, Miles 228b59d). Correctness preflight passed research_exchange.correctness validator (AdamW vs torch AdamW max_abs 2.9e-…
5. Nov 3, 21:51 UTC · running · Launch instructions downloaded, SHA256 verified, fully read. Shared Spindle rejects Dimuon (HTTP 400), so a throwaway fork spindle-fork-run_739abc9a8b (Spindle PR #85 branch + Miles PR #17 dimuon-lit…

adaptation_manifests

[]

correctness_preflight

{'required': True, 'paths': [{'id': 'spindle_dimuon_lite_optimizer', 'fixed_input': {'sha256': 'd989d2d28d1a32103662ca010d0652a1e0ec111b370e20df63a9cf7c0aaed3a8', 'description': 'Step-1 fixture: seed-0 first no_robots train batch (32 seqs, sha256 d9ce572e4cf770c21e54e31f0cd523d578343475316b862f1453171ae1c3c6eb, cross_entropy, cookbook weights), fork init state tinker://43f94846bfb916692227a10acb4ae578/weights/pf-init (fp32 sha256 3388743efeef0e9eb77f61bc1fc018db7bb82e5e6a313f707ad028d9b95e1bd0, LoRA B=0), optimizer state reset per client. Hash covers the exact fp32 gradient fixture (AdamW client exp_avg). Stale-scale step 2 uses a second fixed fp32 gradient. Retrieved via modal volume get run_739abc9a8b-checkpoints.'}, 'baseline_revision': 'UNRESOLVED: pin the modal-projects/spindle commit deployed at the shared Spindle URL (post-#85). AdamW updates on this deployment must match the pre-#85 AdamW update on identical grads/state.', 'reference_revision': "UNRESOLVED: pin the commit of Aaron's DiMuon reconstruction (0.09% relative update error vs Tinker) and the modal-projects/miles commit of #17", 'resolved_revisions': {'baseline_executed': 'fork spindle-fork-run_739abc9a8b: modal-projects/spindle 5148cb62a019862ef9b50d76b7be55cddb288e8a (PR #85 branch claude/dimuon-spindle-wiring) + spindle_fork.patch (requires_proxy_auth=True); shared Spindle deployment 4af4dd2c46c009fcf8661e625195d4bf6c5dde81 is pre-#85 (rejects dimuon), so no post-#85 shared deployment exists. AdamW reference = stock torch.optim.AdamW on identical exp_avg-derived gradient and init.', 'reference_executed': "Aaron's DiMuon reconstruction has no git commit; pinned by content sha256 31c5f60cb273ca53a0adfda9b88b8ff8fd1efd05c497114dbd0cb1f99673c51e (dimuon_candidate.py, code block of Notion page 3fe1e7f169498132903de1b12bfe4205). Miles PR #17 = modal-projects/miles 228b59d90074e6cec48090e033202c5a4aff3ff3 (dimuon.py sha256 8c5268543094cc6677a4606ecbd5902749c34a5e2f95fa7ba29890e732e86519), mounted via SPINDLE_MILES_TREE."}, 'implementation_revision': 'spindle 5148cb62a019862ef9b50d76b7be55cddb288e8a+proxy-auth patch; miles 228b59d90074e6cec48090e033202c5a4aff3ff3 (dimuon.py sha256 8c5268543094cc6677a4606ecbd5902749c34a5e2f95fa7ba29890e732e86519)', 'precision': 'fp32 master params and gradients; Dimuon working momentum fp32, persisted momentum bf16, Polar Express 5 iters horner_fp16; AdamW fp32 exp_avg/exp_avg_sq; model forward/backward bf16', 'normalization': 'cross_entropy sum of weighted token NLL (cookbook weights, counted by weight>0); Dimuon per-projection-block direction scaled to Frobenius sqrt(rank=32), update lr*(32/32)^0.175, damping 1e-4, beta1 0.9, no grad clip; AdamW bias-corrected first step lr*m_hat/(sqrt(v_hat)+1e-8)', 'errors': {'baseline_update': {'max_abs_error': 2.9103830456733704e-11, 'reference_max_abs': 0.00010000001202570274, 'relative_l2_error': 1.1838959207204277e-07, 'cosine_similarity': 0.9999999999984652}, 'reference_update': {'max_abs_error': 0.0, 'reference_max_abs': 0.00043169609853066504, 'relative_l2_error': 0.0, 'cosine_similarity': 0.9999999999996416}}, 'diagnostic_errors': {'reference_update_stale_scales_step2_packed_vs_unpacked': {'max_abs_error': 5.820766091346741e-11, 'reference_max_abs': 0.0004482679651118815, 'relative_l2_error': 7.763380802737512e-08, 'cosine_similarity': 1.000000000005693}, 'server_dimuon_vs_reference_bf16_momentum_reconstructed_grad': {'max_abs_error': 1.619606337044388e-05, 'reference_max_abs': 0.00043122234637849033, 'relative_l2_error': 0.008914503668966067, 'cosine_similarity': 0.999960265766336}, 'server_dimuon_vs_local_miles_same_bf16_input': {'max_abs_error': 1.619606337044388e-05, 'reference_max_abs': 0.00043122234637849033, 'relative_l2_error': 0.008914503668966067, 'cosine_similarity': 0.9999602657663412}, 'server_dimuon_vs_reference_other_client_fp32_grad': {'max_abs_error': 1.3311757356859744e-05, 'reference_max_abs': 0.00043169609853066504, 'relative_l2_error': 0.01112447649318528, 'cosine_similarity': 0.9999381229397716}, 'server_cross_client_noise_floor_lr1e-4x10_vs_lr1e-3': {'max_abs_error': 1.1148425983265042e-05, 'reference_max_abs': 0.0004318455175962299, 'relative_l2_error': 0.008873499902993041, 'cosine_similarity': 0.9999606304975468}, 'server_cross_client_gradient_agreement': {'max_abs_error': 0.003906428813934326, 'reference_max_abs': 0.9726564288139344, 'relative_l2_error': 0.00445943772141835, 'cosine_similarity': 0.9999900624438804}, 'restore_replay_update_vs_uninterrupted': {'max_abs_error': 2.4998094886541367e-05, 'reference_max_abs': 0.000484729214804247, 'relative_l2_error': 0.009888607172079344, 'cosine_similarity': 0.999951107737341}}, 'reference_update_note': "reference_update compares the deployed Miles Dimuon code (packed fused qkv/fc1 B) against Aaron's reference (unpacked per-projection blocks) on bitwise-identical fp32 inputs. The server-executed update cannot be fed an identical fp32 gradient; vs reference it shows max_abs 1.62e-5 (> 1.43e-5 threshold) with bf16-reconstructed input, identical to its error vs local Miles code on the same input, and 1.33e-5 (passes) with another client's fp32 gradient; server cross-client noise floor is 1.11e-5.", 'lr_scaling': [{'learning_rate': 0.0001, 'update_norm': 0.005571355047044043}, {'learning_rate': 0.001, 'update_norm': 0.05571355442863166}, {'learning_rate': 0.01, 'update_norm': 0.5571355017914699}]}]}

correctness_validation

{'status': 'passed', 'required': True, 'checks': [{'path': 'spindle_dimuon_lite_optimizer', 'check': 'baseline_update', 'status': 'passed', 'tolerance': {'atol': 1e-06, 'rtol': 0.001, 'min_cosine': 0.99999}, 'measured': {'max_abs_error': 2.9103830456733704e-11, 'reference_max_abs': 0.00010000001202570274, 'relative_l2_error': 1.1838959207204277e-07, 'cosine_similarity': 0.9999999999984652}}, {'path': 'spindle_dimuon_lite_optimizer', 'check': 'reference_update', 'status': 'passed', 'tolerance': {'atol': 1e-05, 'rtol': 0.01, 'min_cosine': 0.999}, 'measured': {'max_abs_error': 0.0, 'reference_max_abs': 0.00043169609853066504, 'relative_l2_error': 0.0, 'cosine_similarity': 0.9999999999996416}}, {'path': 'spindle_dimuon_lite_optimizer', 'check': 'lr_scaling', 'status': 'passed', 'relative_error': 7.10453955665713e-08, 'tolerance': {'learning_rates': [0.0001, 0.001, 0.01], 'rtol': 0.01}}], 'reason': 'Reported baseline and reference parity checks meet accepted tolerances.', 'evidence_source': 'executor report; remote execution is not attested', 'applicability': 'required', 'reported_verdict': 'inconclusive'}

decision

{'version': 1, 'observations_file': {'name': 'scored-observations-final.jsonl', 'sha256': '620c1d8cea254438c9e1d250282fcd7b4fb4aba7fd4a919569d089c9f1d78faf'}, 'selection': {'frozen_before_confirmation': True, 'selection_split': '500 held-out no_robots train rows (Random(1234) shuffle, first 500), disjoint from training rows', 'selection_steps': 40, 'adamw': {'scores': {'5e-05': 2.0706255712024144, '0.0001': 2.0661315833516616, '0.0002': 2.063218484765968}, 'selected_lr': 0.0002, 'edge': True}, 'dimuon': {'scores': {'0.0003': 2.250428290149688, '0.001': 2.1062885413010375, '0.003': 2.0673232168471443}, 'selected_lr': 0.003, 'edge': True}, 'grid_edge_selected': True}}

decision_validation

{'status': 'uncertain', 'issues': ['Adaptive selection requires disjoint search and confirmation IDs.'], 'criteria': [], 'outcome_reason': 'insufficient_evidence', 'observations_file': {'name': 'scored-observations-final.jsonl', 'sha256': '620c1d8cea254438c9e1d250282fcd7b4fb4aba7fd4a919569d089c9f1d78faf', 'size': 1501, 'n_observations': 6}, 'unit': 'training_seed', 'n_seeds': 3, 'alpha': 0.05, 'method': 'paired_t', 'assumptions': {'independent_seed_effects': True, 'identically_distributed_seed_effects': True, 'normal_seed_effects': True}, 'applicability': {'check': 'decision_rule', 'status': 'required', 'enforce': True, 'reasons': ['experiment_features: paired_comparison, adaptive_selection is true'], 'features': {'paired_comparison': True, 'adaptive_selection': True}}}

evaluation

{'version': 1, 'evaluators': {'a4a7f46a521c63887b23a3366c4f5246f66c98d26fe8bdeea93b6303b1555744': {'code_sha256': '52b3d966475a14ba5b3d854d7e22134648b12c59b47ca24801c3b7f66d34fb90', 'prompt_template': 'tinker_cookbook renderer for Qwen/Qwen3.5-2B (renderers.get_recommended_renderer_name), conversation_to_datum(messages, renderer, max_length=2048), TrainOnWhat=ALL_ASSISTANT_MESSAGES default; teacher-forced cross_entropy forward; NLL counted where weight>0', 'decoding': {'type': 'teacher_forced_forward', 'temperature': None, 'max_tokens': 0, 'stop': []}, 'parser': 'token NLL = -sum(logprobs[weights>0]); token_mean = sum NLL / sum counted tokens over 500 items', 'items': {'ids': ['no_robots_test:d6c011ffb1ff8a9abe9bd24caf3f9817454a1f054d5d0e0360d19bf50cf6b20c', 'no_robots_test:7682c850c4e5979cef0ad966b8feb119de2bd323bb40819767a161040b59eb7a', 'no_robots_test:45971bbe382b81644487690d2018bc3aec8c807f1be52a9744223e5492255928', 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'no_robots_test:602272a99566bf68a5b48979478bceb0b56f74915925ed8a1b0a558935df3584', 'no_robots_test:ecdac8f179582eb850ec28155ea59279e445c219d7bcccc6753cef24a7199a42', 'no_robots_test:46b880155fdd947f395a3009a313bf8cbfe8e137c37b3a614c49585112e0aa0f', 'no_robots_test:65b835d2335e23cf908b10817be1eb5c1bd5dcd3b80bd3e7a46c72d2cd479e8d', 'no_robots_test:48363ab6108604ad6812a9dbe0f17fc52dc68532d3b3bf4d83bfadcea7b99f9a']}}}, 'scores': [{'arm': 'baseline', 'seed': 0, 'metric': 'no_robots_test_token_mean_nll_at_step_100', 'value': 2.0330268793696935, 'evaluator_id': 'a4a7f46a521c63887b23a3366c4f5246f66c98d26fe8bdeea93b6303b1555744', 'predictions_ref': 'predictions-confirm-test.jsonl'}, {'arm': 'treatment', 'seed': 0, 'metric': 'no_robots_test_token_mean_nll_at_step_100', 'value': 2.0323170273395057, 'evaluator_id': 'a4a7f46a521c63887b23a3366c4f5246f66c98d26fe8bdeea93b6303b1555744', 'predictions_ref': 'predictions-confirm-test.jsonl'}, {'arm': 'baseline', 'seed': 1, 'metric': 'no_robots_test_token_mean_nll_at_step_100', 'value': 2.0315072699517907, 'evaluator_id': 'a4a7f46a521c63887b23a3366c4f5246f66c98d26fe8bdeea93b6303b1555744', 'predictions_ref': 'predictions-confirm-test.jsonl'}, {'arm': 'treatment', 'seed': 1, 'metric': 'no_robots_test_token_mean_nll_at_step_100', 'value': 2.0304049240877835, 'evaluator_id': 'a4a7f46a521c63887b23a3366c4f5246f66c98d26fe8bdeea93b6303b1555744', 'predictions_ref': 'predictions-confirm-test.jsonl'}, {'arm': 'baseline', 'seed': 2, 'metric': 'no_robots_test_token_mean_nll_at_step_100', 'value': 2.030763545006581, 'evaluator_id': 'a4a7f46a521c63887b23a3366c4f5246f66c98d26fe8bdeea93b6303b1555744', 'predictions_ref': 'predictions-confirm-test.jsonl'}, {'arm': 'treatment', 'seed': 2, 'metric': 'no_robots_test_token_mean_nll_at_step_100', 'value': 2.030674625589407, 'evaluator_id': 'a4a7f46a521c63887b23a3366c4f5246f66c98d26fe8bdeea93b6303b1555744', 'predictions_ref': 'predictions-confirm-test.jsonl'}], 'comparisons': [{'metric': 'no_robots_test_token_mean_nll_at_step_100', 'arms': ['baseline', 'treatment']}]}

evaluator_validation

{'status': 'comparable', 'issues': [], 'comparisons': [{'metric': 'no_robots_test_token_mean_nll_at_step_100', 'status': 'comparable', 'arms': ['baseline', 'treatment'], 'evaluator_ids': ['a4a7f46a521c63887b23a3366c4f5246f66c98d26fe8bdeea93b6303b1555744'], 'missing_arms': []}]}

feasibility

{'status': 'unknown', 'problems': ['Re-projection required: lora_rank changed or was not recorded', 'Re-projection required: dataset changed or was not recorded', 'Re-projection required: steps changed or was not recorded', 'Re-projection required: n_examples changed or was not recorded', 'Re-projection required: n_eval changed or was not recorded', 'Representative evaluation timing not recorded', 'Representative setup timing not recorded', 'Representative training timing not recorded', 'Unknown timing phase: setup_pilot', 'Unknown timing phase: training_pilot', 'Unknown timing phase: heldout_eval_pilot', 'Unknown timing phase: setup_selection', 'Unknown timing phase: training_selection', 'Unknown timing phase: heldout_eval_selection', 'Unknown timing phase: setup_confirmation', 'Unknown timing phase: training_confirmation', 'Unknown timing phase: heldout_eval_confirmation'], 'wall_seconds': None, 'gpu_hours': None, 'known_gpu_hours': 1.3603800910049015, 'phases': {'recovery': {'wall_seconds': 4897.368327617645, 'gpu_hours': 1.3603800910049015}}, 'generation_counts': None, 'generation_applicability': {'check': 'generation_audit', 'status': 'not_applicable', 'enforce': False, 'reasons': ['spec.generation_audit opts out and no feature it covers is true'], 'features': {'generated_training_data': False}}, 'record': {'config': {'base_model': 'Qwen/Qwen3.5-2B', 'arms': 12, 'selection_steps': 40, 'confirmation_steps': 100, 'batch_size': 32, 'max_seq_len': 2048, 'n_eval_validation': 500, 'n_eval_test': 500, 'n_ifeval_secondary': 541, 'planned_arm_steps': 840}, 'effective_config': {'base_model': 'Qwen/Qwen3.5-2B', 'arms': 12, 'selection_steps': 40, 'confirmation_steps': 100, 'batch_size': 32, 'max_seq_len': 2048, 'n_eval_validation': 500, 'n_eval_test': 500, 'n_ifeval_secondary': 0, 'planned_arm_steps': 840, 'pilot_arms': 2, 'pilot_steps': 10, 'note': 'IFEval dropped (downsizing step 1); LR grid, selection steps, seeds, test NLL unchanged'}, 'primary_endpoint': 'no_robots_test_token_mean_nll_at_step_100', 'source': 'pilot_log.jsonl (10-step 2-arm representative pilot); select_log.jsonl and confirm_log.jsonl measured', 'measurements': {'setup_pilot': {'seconds': 252.8196606636047, 'units': 2, 'planned_units': 2, 'samples': 2, 'gpu_count': 1, 'allocation_source': 'fork_config.py trainer_gpus_per_node=1 (single trainer GPU; inference replicas 0)'}, 'training_pilot': {'seconds': 161.3649604320526, 'units': 20, 'planned_units': 20, 'samples': 640, 'gpu_count': 1, 'allocation_source': 'fork_config.py trainer_gpus_per_node=1 (single trainer GPU; inference replicas 0)'}, 'heldout_eval_pilot': {'seconds': 80.4703094959259, 'units': 1000, 'planned_units': 1000, 'samples': 1000, 'gpu_count': 1, 'allocation_source': 'fork_config.py trainer_gpus_per_node=1 (single trainer GPU; inference replicas 0)'}, 'setup_selection': {'seconds': 48.95686173439026, 'units': 6, 'planned_units': 6, 'samples': 6, 'gpu_count': 1, 'allocation_source': 'fork_config.py trainer_gpus_per_node=1 (single trainer GPU; inference replicas 0)'}, 'training_selection': {'seconds': 749.6906819343567, 'units': 240, 'planned_units': 240, 'samples': 7680, 'gpu_count': 1, 'allocation_source': 'fork_config.py trainer_gpus_per_node=1 (single trainer GPU; inference replicas 0)'}, 'heldout_eval_selection': {'seconds': 127.7629702091217, 'units': 3000, 'planned_units': 3000, 'samples': 3000, 'gpu_count': 1, 'allocation_source': 'fork_config.py trainer_gpus_per_node=1 (single trainer GPU; inference replicas 0)'}, 'setup_confirmation': {'seconds': 266.957839012146, 'units': 6, 'planned_units': 6, 'samples': 6, 'gpu_count': 1, 'allocation_source': 'fork_config.py trainer_gpus_per_node=1 (single trainer GPU; inference replicas 0)'}, 'training_confirmation': {'seconds': 786.5640952587128, 'units': 600, 'planned_units': 600, 'samples': 19200, 'gpu_count': 1, 'allocation_source': 'fork_config.py trainer_gpus_per_node=1 (single trainer GPU; inference replicas 0)'}, 'heldout_eval_confirmation': {'seconds': 134.40599060058594, 'units': 3000, 'planned_units': 3000, 'samples': 3000, 'gpu_count': 1, 'allocation_source': 'fork_config.py trainer_gpus_per_node=1 (single trainer GPU; inference replicas 0)'}}, 'generation': None, 'recovery': {'seconds': 4897.368327617645, 'gpu_count': 1, 'allocation_source': 'fork_config.py trainer_gpus_per_node=1 (single trainer GPU; inference replicas 0)', 'note': 'wall used 68.4 of 150 min; trainer GPU-h used ~1.02 of 2.0 (deploy to now, 1 GPU)'}}, 'recorded_at': 1793745777.0760796}

feasibility_required

True

generation_audit_validation

{'status': 'not_applicable', 'reasons': ['spec.generation_audit opts out and no feature it covers is true'], 'applicability': {'check': 'generation_audit', 'status': 'not_applicable', 'enforce': False, 'reasons': ['spec.generation_audit opts out and no feature it covers is true'], 'features': {'generated_training_data': False}}}

initialization_provenance

{'version': 1, 'arms': [{'arm': 'sel_adamw_lr5e-05_s0', 'phase': 'select', 'base_checkpoint': 'Qwen/Qwen3.5-2B', 'initialization_checkpoint': 'tinker://f23fad8a5cc3c0a7883ead5d7fb0188c/weights/select-init-s0', 'before_first_update': True, 'restore_count': 0, 'optimizer_checks': [{'restores': 0, 'optimizer': 'adamw', 'model': 'Qwen/Qwen3.5-2B', 'rank': 32}], 'initial_output_sha256': 'd7ba8bf1d6c57c98e0583ccf7b1768634e9973631561c4770d0e69e1e8fee0f7', 'evidence_ref': 'select_log.jsonl', 'adapter_sha256': None, 'adapter_note': 'selection arms all restored from one saved state select-init-s0; adapter hash not downloaded per arm, only initial output hash'}, {'arm': 'sel_adamw_lr0.0001_s0', 'phase': 'select', 'base_checkpoint': 'Qwen/Qwen3.5-2B', 'initialization_checkpoint': 'tinker://f23fad8a5cc3c0a7883ead5d7fb0188c/weights/select-init-s0', 'before_first_update': True, 'restore_count': 1, 'optimizer_checks': [{'restores': 0, 'optimizer': 'adamw', 'model': 'Qwen/Qwen3.5-2B', 'rank': 32}, {'restores': 1, 'optimizer': 'adamw', 'model': 'Qwen/Qwen3.5-2B', 'rank': 32}], 'initial_output_sha256': 'd7ba8bf1d6c57c98e0583ccf7b1768634e9973631561c4770d0e69e1e8fee0f7', 'evidence_ref': 'select_log.jsonl', 'adapter_sha256': None, 'adapter_note': 'selection arms all restored from one saved state select-init-s0; adapter hash not downloaded per arm, only initial output hash'}, {'arm': 'sel_adamw_lr0.0002_s0', 'phase': 'select', 'base_checkpoint': 'Qwen/Qwen3.5-2B', 'initialization_checkpoint': 'tinker://f23fad8a5cc3c0a7883ead5d7fb0188c/weights/select-init-s0', 'before_first_update': True, 'restore_count': 1, 'optimizer_checks': [{'restores': 0, 'optimizer': 'adamw', 'model': 'Qwen/Qwen3.5-2B', 'rank': 32}, {'restores': 1, 'optimizer': 'adamw', 'model': 'Qwen/Qwen3.5-2B', 'rank': 32}], 'initial_output_sha256': 'd7ba8bf1d6c57c98e0583ccf7b1768634e9973631561c4770d0e69e1e8fee0f7', 'evidence_ref': 'select_log.jsonl', 'adapter_sha256': None, 'adapter_note': 'selection arms all restored from one saved state select-init-s0; adapter hash not downloaded per arm, only initial output hash'}, {'arm': 'sel_dimuon_lr0.0003_s0', 'phase': 'select', 'base_checkpoint': 'Qwen/Qwen3.5-2B', 'initialization_checkpoint': 'tinker://f23fad8a5cc3c0a7883ead5d7fb0188c/weights/select-init-s0', 'before_first_update': True, 'restore_count': 1, 'optimizer_checks': [{'restores': 0, 'optimizer': 'dimuon', 'model': 'Qwen/Qwen3.5-2B', 'rank': 32}, {'restores': 1, 'optimizer': 'dimuon', 'model': 'Qwen/Qwen3.5-2B', 'rank': 32}], 'initial_output_sha256': 'd7ba8bf1d6c57c98e0583ccf7b1768634e9973631561c4770d0e69e1e8fee0f7', 'evidence_ref': 'select_log.jsonl', 'adapter_sha256': None, 'adapter_note': 'selection arms all restored from one saved state select-init-s0; adapter hash not downloaded per arm, only initial output hash'}, {'arm': 'sel_dimuon_lr0.001_s0', 'phase': 'select', 'base_checkpoint': 'Qwen/Qwen3.5-2B', 'initialization_checkpoint': 'tinker://f23fad8a5cc3c0a7883ead5d7fb0188c/weights/select-init-s0', 'before_first_update': True, 'restore_count': 1, 'optimizer_checks': [{'restores': 0, 'optimizer': 'dimuon', 'model': 'Qwen/Qwen3.5-2B', 'rank': 32}, {'restores': 1, 'optimizer': 'dimuon', 'model': 'Qwen/Qwen3.5-2B', 'rank': 32}], 'initial_output_sha256': 'd7ba8bf1d6c57c98e0583ccf7b1768634e9973631561c4770d0e69e1e8fee0f7', 'evidence_ref': 'select_log.jsonl', 'adapter_sha256': None, 'adapter_note': 'selection arms all restored from one saved state select-init-s0; adapter hash not downloaded per arm, only initial output hash'}, {'arm': 'sel_dimuon_lr0.003_s0', 'phase': 'select', 'base_checkpoint': 'Qwen/Qwen3.5-2B', 'initialization_checkpoint': 'tinker://f23fad8a5cc3c0a7883ead5d7fb0188c/weights/select-init-s0', 'before_first_update': True, 'restore_count': 1, 'optimizer_checks': [{'restores': 0, 'optimizer': 'dimuon', 'model': 'Qwen/Qwen3.5-2B', 'rank': 32}, {'restores': 1, 'optimizer': 'dimuon', 'model': 'Qwen/Qwen3.5-2B', 'rank': 32}], 'initial_output_sha256': 'd7ba8bf1d6c57c98e0583ccf7b1768634e9973631561c4770d0e69e1e8fee0f7', 'evidence_ref': 'select_log.jsonl', 'adapter_sha256': None, 'adapter_note': 'selection arms all restored from one saved state select-init-s0; adapter hash not downloaded per arm, only initial output hash'}, {'arm': 'baseline_adamw_s0', 'phase': 'confirm', 'base_checkpoint': 'Qwen/Qwen3.5-2B', 'initialization_checkpoint': 'tinker://b61dfd929fd09745b512c7224450cbde/weights/confirm-init-s0', 'before_first_update': True, 'restore_count': 0, 'optimizer_checks': [{'restores': 0, 'optimizer': 'adamw', 'model': 'Qwen/Qwen3.5-2B', 'rank': 32}], 'initial_output_sha256': '6e2313c4c269801a8b9950b4ebee38fbda850114d334dccc1ea46abd084e51bc', 'evidence_ref': 'restore_check.json', 'step0_checkpoint': 'tinker://b61dfd929fd09745b512c7224450cbde/weights/confirm-baseline_adamw_s0-step0', 'adapter_sha256': '380d38d8c1f3dde5e19b20aa642b3e30986c4b68738a2ec20df210e40caa8b10', 'fp32_param_sha256': 'cffcdbe0c5da48f60f9d55d7bcfc4d55931425af27f6c12a6185b0c029d7d089', 'init_adapter_sha256': '380d38d8c1f3dde5e19b20aa642b3e30986c4b68738a2ec20df210e40caa8b10', 'adapter_matches_init': True, 'server_checkpoint_optimizer': 'adamw'}, {'arm': 'treatment_dimuon_s0', 'phase': 'confirm', 'base_checkpoint': 'Qwen/Qwen3.5-2B', 'initialization_checkpoint': 'tinker://b61dfd929fd09745b512c7224450cbde/weights/confirm-init-s0', 'before_first_update': True, 'restore_count': 1, 'optimizer_checks': [{'restores': 0, 'optimizer': 'dimuon', 'model': 'Qwen/Qwen3.5-2B', 'rank': 32}, {'restores': 1, 'optimizer': 'dimuon', 'model': 'Qwen/Qwen3.5-2B', 'rank': 32}], 'initial_output_sha256': '6e2313c4c269801a8b9950b4ebee38fbda850114d334dccc1ea46abd084e51bc', 'evidence_ref': 'restore_check.json', 'step0_checkpoint': 'tinker://d331297013cae43230697585afa80dc5/weights/confirm-treatment_dimuon_s0-step0', 'adapter_sha256': '380d38d8c1f3dde5e19b20aa642b3e30986c4b68738a2ec20df210e40caa8b10', 'fp32_param_sha256': 'cffcdbe0c5da48f60f9d55d7bcfc4d55931425af27f6c12a6185b0c029d7d089', 'init_adapter_sha256': '380d38d8c1f3dde5e19b20aa642b3e30986c4b68738a2ec20df210e40caa8b10', 'adapter_matches_init': True, 'server_checkpoint_optimizer': 'dimuon'}, {'arm': 'baseline_adamw_s1', 'phase': 'confirm', 'base_checkpoint': 'Qwen/Qwen3.5-2B', 'initialization_checkpoint': 'tinker://9c18b804f21a06705c6da69577f80b4e/weights/confirm-init-s1', 'before_first_update': True, 'restore_count': 0, 'optimizer_checks': [{'restores': 0, 'optimizer': 'adamw', 'model': 'Qwen/Qwen3.5-2B', 'rank': 32}], 'initial_output_sha256': '6e2313c4c269801a8b9950b4ebee38fbda850114d334dccc1ea46abd084e51bc', 'evidence_ref': 'restore_check.json', 'step0_checkpoint': 'tinker://9c18b804f21a06705c6da69577f80b4e/weights/confirm-baseline_adamw_s1-step0', 'adapter_sha256': 'c8a11ec8d00edf4b6b45d3feac3e799de096e46a9e3176aac67fdb14f0d8592b', 'fp32_param_sha256': 'fb777da03b0c3b627613a59d27d60508134ad05cb58b83a04a598c9b9c7d1908', 'init_adapter_sha256': 'c8a11ec8d00edf4b6b45d3feac3e799de096e46a9e3176aac67fdb14f0d8592b', 'adapter_matches_init': True, 'server_checkpoint_optimizer': 'adamw'}, {'arm': 'treatment_dimuon_s1', 'phase': 'confirm', 'base_checkpoint': 'Qwen/Qwen3.5-2B', 'initialization_checkpoint': 'tinker://9c18b804f21a06705c6da69577f80b4e/weights/confirm-init-s1', 'before_first_update': True, 'restore_count': 1, 'optimizer_checks': [{'restores': 0, 'optimizer': 'dimuon', 'model': 'Qwen/Qwen3.5-2B', 'rank': 32}, {'restores': 1, 'optimizer': 'dimuon', 'model': 'Qwen/Qwen3.5-2B', 'rank': 32}], 'initial_output_sha256': '6e2313c4c269801a8b9950b4ebee38fbda850114d334dccc1ea46abd084e51bc', 'evidence_ref': 'restore_check.json', 'step0_checkpoint': 'tinker://64eea06e33ba66329c486ce2de950f32/weights/confirm-treatment_dimuon_s1-step0', 'adapter_sha256': 'c8a11ec8d00edf4b6b45d3feac3e799de096e46a9e3176aac67fdb14f0d8592b', 'fp32_param_sha256': 'fb777da03b0c3b627613a59d27d60508134ad05cb58b83a04a598c9b9c7d1908', 'init_adapter_sha256': 'c8a11ec8d00edf4b6b45d3feac3e799de096e46a9e3176aac67fdb14f0d8592b', 'adapter_matches_init': True, 'server_checkpoint_optimizer': 'dimuon'}, {'arm': 'baseline_adamw_s2', 'phase': 'confirm', 'base_checkpoint': 'Qwen/Qwen3.5-2B', 'initialization_checkpoint': 'tinker://581c1850d38ffa76474e2c6c7f2a3fb1/weights/confirm-init-s2', 'before_first_update': True, 'restore_count': 0, 'optimizer_checks': [{'restores': 0, 'optimizer': 'adamw', 'model': 'Qwen/Qwen3.5-2B', 'rank': 32}], 'initial_output_sha256': '6e2313c4c269801a8b9950b4ebee38fbda850114d334dccc1ea46abd084e51bc', 'evidence_ref': 'restore_check.json', 'step0_checkpoint': 'tinker://581c1850d38ffa76474e2c6c7f2a3fb1/weights/confirm-baseline_adamw_s2-step0', 'adapter_sha256': '0c1377ebc59ba6d47f890fe6f1b04c3f019054b02af31da4bbca3f66e0c1fc0a', 'fp32_param_sha256': '6d3b9c3d0659410a206fe85843af48ad08a195d32987ccd2c83c3b6b10bf2db6', 'init_adapter_sha256': '0c1377ebc59ba6d47f890fe6f1b04c3f019054b02af31da4bbca3f66e0c1fc0a', 'adapter_matches_init': True, 'server_checkpoint_optimizer': 'adamw'}, {'arm': 'treatment_dimuon_s2', 'phase': 'confirm', 'base_checkpoint': 'Qwen/Qwen3.5-2B', 'initialization_checkpoint': 'tinker://581c1850d38ffa76474e2c6c7f2a3fb1/weights/confirm-init-s2', 'before_first_update': True, 'restore_count': 1, 'optimizer_checks': [{'restores': 0, 'optimizer': 'dimuon', 'model': 'Qwen/Qwen3.5-2B', 'rank': 32}, {'restores': 1, 'optimizer': 'dimuon', 'model': 'Qwen/Qwen3.5-2B', 'rank': 32}], 'initial_output_sha256': '6e2313c4c269801a8b9950b4ebee38fbda850114d334dccc1ea46abd084e51bc', 'evidence_ref': 'restore_check.json', 'step0_checkpoint': 'tinker://8e9fef4c99e9c1af8d68a14c2c921011/weights/confirm-treatment_dimuon_s2-step0', 'adapter_sha256': '0c1377ebc59ba6d47f890fe6f1b04c3f019054b02af31da4bbca3f66e0c1fc0a', 'fp32_param_sha256': '6d3b9c3d0659410a206fe85843af48ad08a195d32987ccd2c83c3b6b10bf2db6', 'init_adapter_sha256': '0c1377ebc59ba6d47f890fe6f1b04c3f019054b02af31da4bbca3f66e0c1fc0a', 'adapter_matches_init': True, 'server_checkpoint_optimizer': 'dimuon'}], 'paired_init_matched_all_seeds': True}

launch

{'experiment_id': 'exp_71911f0fb9', 'context_status': 'snapshotted', 'superseded': False, 'mode': 'ordinary', 'purpose': 'Execute the selected saved experiment version.'}

launch_instructions

{'name': 'launch-instructions-8e52fc8e31fe.md', 'sha256': '8e52fc8e31fecddfff670ff7003e766ab25bb9c42a201d33a1edc5ccea1d95ea', 'url': 'https://ta-01m6fva5db548f5skm214fepcr-8000-oah9l7x00jpo7186eb6cix5v0.w.modal.host/api/runs/run_739abc9a8b/evidence/8e52fc8e31fecddfff670ff7003e766ab25bb9c42a201d33a1edc5ccea1d95ea'}

reported_outcome_reason

insufficient_evidence

reported_verdict

inconclusive

restoration_validation

{'status': 'unverified', 'required': True, 'checks': [{'check': 'baseline_adamw_s0', 'status': 'unverified', 'reason': 'baseline_adamw_s0: missing or duplicate arm evidence.'}, {'check': 'treatment_dimuon_s0', 'status': 'unverified', 'reason': 'treatment_dimuon_s0: missing or duplicate arm evidence.'}, {'check': 'baseline_adamw_s1', 'status': 'unverified', 'reason': 'baseline_adamw_s1: missing or duplicate arm evidence.'}, {'check': 'treatment_dimuon_s1', 'status': 'unverified', 'reason': 'treatment_dimuon_s1: missing or duplicate arm evidence.'}, {'check': 'baseline_adamw_s2', 'status': 'unverified', 'reason': 'baseline_adamw_s2: missing or duplicate arm evidence.'}, {'check': 'treatment_dimuon_s2', 'status': 'unverified', 'reason': 'treatment_dimuon_s2: missing or duplicate arm evidence.'}], 'reason': 'baseline_adamw_s0: missing or duplicate arm evidence.', 'applicability': 'required'}