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johnnynunez
f42cdcf137
Evidence from the LeRobot-vs-OSS checkpoint comparison: the LeRobot/HF 8k checkpoint's DiT moved only ~19% as far from base as the OSS-trained one (0.0547 vs 0.285 relative L2) - undertrained because the scheduler decayed over a hardcoded 10k steps regardless of --steps, on top of beta1/clip mismatches. - AdamW betas (0.95, 0.999) -> (0.9, 0.999) and grad_clip_norm 10.0 -> 1.0 (Isaac defaults) - scheduler: hardcoded CosineDecayWithWarmup(10k decay, floor 10% peak) -> DiffuserSchedulerConfig HF cosine with ceil(max_steps * warmup_ratio) warmup, deriving num_training_steps from the outer --steps at runtime - model_params_fp32 (default true): keep master weights in FP32 and compute under BF16 autocast like the native N1.7 recipe (fixes optimizer-update numerics vs pure-BF16 params) - weight-decay grouping via transformers get_parameter_names: biases and norm parameters excluded from decay - restore the TF4 lm_head/embedding weight tie so the unused Qwen LM head stays frozen and deduplicated in checkpoints - action_mask kept in native dtype for the masked flow-matching loss - drop_n_last_frames: exclude episode tails that cannot supply a complete action chunk (Isaac sampler behavior) Verification: tests/policies/groot/test_groot_training_optim_contract.py (7 passed) + remaining groot suite 11 passed/5 skipped on RTX PRO 6000 / CUDA 13.3. Note: tests/policies/groot/test_groot_n1_7.py does not collect on the base branch (pre-existing ImportError, fixed in PR #37).