[WIP] Controller on datapacker - #2132
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PengchengShi00 wants to merge 11 commits into
Open
PengchengShi00 wants to merge 11 commits into
PengchengShi00 wants to merge 11 commits into
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…troller - AgentLoop.canonicalize_train_fields (base + localhost/sandbox overrides) builds the unified full-sequence train fields (input_ids/labels/logprobs) at generation time; semantic holes are baked into labels by the loops. - RolloutState drops response_mask; labels become the only supervision carrier. agent_loop_type records the producing loop class name and AGENTIC_AGENT_LOOP_TYPES discriminates agentic full-sequence samples. - TrainingController.fit accepts list[list[RolloutState]] and absorbs validation, session-clustered advantages, shift/tensorization, seq_ctx, teacher fields and data_info; BaseRLTrainer._prepare_train_data is gone. - calculate_group_effective_response_masks now bakes token staleness into labels in place (monotone, convergent) and its agentic exclusion uses agent_loop_type instead of the input_ids/labels presence heuristic.
…ut conversion - response_ids now denotes the contiguous suffix of input_ids after the prompt (env/tool tokens included) in every loop; localhost/sandbox export input_ids[len(prompt_ids):] so response_model_steps stay aligned with the full response region. - Token staleness baking becomes branch-free: effective mask = semantic mask (labels != -100 on the suffix) * per-token staleness mask; the zero-prompt suffix state becomes eligible and agent_loop_type plus AGENTIC_AGENT_LOOP_TYPES are removed. - data_info stats: prompt_len reports the original prompt length and response_len the supervised (LLM-generated) token count; env/tool injected tokens count in neither. - _rollout_groups_to_colate_items is phase-split into session reward clustering, group advantage estimation, per-state ColateItem conversion and data_info summarization.
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