Match the working SmolVLA2 launch pattern so the two SLURM scripts
are interchangeable:
* literal NUM_PROCESSES / BATCH_SIZE / STEPS (no env-var defaults)
* STEPS=10000 to match the next SmolVLA2 run
* save_freq=$STEPS so only the final checkpoint is saved
* dropouts 0.1/0.1/0.1 (mild — matches the operator's iteration)
* flow_loss_weight / text_loss_weight come from the PI052Config
defaults (10.0 / 1.0 per Pi 0.5 paper §IV.D), no need to pass
them explicitly
Job name and policy_repo_id mirror the SmolVLA2 ``_tool-g2`` naming
so the two runs can be compared side-by-side in WandB.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
New ``lerobot.policies.pi052`` (parallel to ``smolvla2``) that adds
text-prediction + hierarchical-inference on top of the existing π0.5
implementation. Mirrors the paper's §IV.D dual-head training:
L = H(text) + α * ‖ω - a - f_θ_action(...)‖², α = 10
Components:
* ``configuration_pi052.py`` thin PI05Config subclass; adds
recipe_path, text/flow loss weights
(default α=10 per paper), prompt
dropout knobs, ``unfreeze_lm_head``.
* ``text_processor_pi052.py`` PI052TextTokenizerStep — concatenates
rendered messages as ``Role: ...``
plain text (PaliGemma has no chat
template), tokenises with the
PaliGemma tokenizer, builds a label
mask covering supervised target
spans. Includes Pi 0.7 §V.E
per-component prompt dropout.
* ``processor_pi052.py`` make_pi052_pre_post_processors —
Rename + Batch + Relative +
Normalize + RenderMessagesStep +
PI052TextTokenizerStep + Device.
Falls back to π0.5's plain pipeline
when recipe_path is unset.
* ``modeling_pi052.py`` PI052Policy(PI05Policy) — re-enables
PaliGemma ``lm_head``, computes
text_loss via CE on the supervised
span, sums with flow_loss in
forward(), and adds select_message
for AR text generation at inference
(same surface as
SmolVLA2Policy.select_message so
SmolVLA2Runtime drives it unchanged).
Plus the supporting plumbing:
* recipe ``configs/recipes/pi052_hirobot.yaml`` — same Hi-Robot blend
as smolvla2_hirobot.yaml, with the same ``${subtask}`` /
``if_present`` supervision fix (current span at every frame, not
``${next_subtask}``).
* SLURM ``examples/training/pi052_hirobot.slurm`` — full training
command matching the SmolVLA2 launcher.
* factory registration: ``--policy.type=pi052`` resolves to
PI052Policy with the new processor.
Same multi-rate runtime (``lerobot.policies.smolvla2.inference``)
drives this policy too — both expose ``predict_action_chunk`` for the
action expert and ``select_message`` for the LM head.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Pepijn