Support challenger training and support continous sampling - #280
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- config.py: E13 executor_thinking='off' to match the SEAM paper run - run_ablate12.sh: dedicated E13 block (min_level=0 full pool, chunk=128, reward-trunc-penalty=0, eval R=1/T=0) reproducing the SEAM run config - include the code-task/reflexion pipeline modules E13 imports at load time (main/trainer top-level import code_task/data_code/eval_reflexion)
…form) - train_skill_v2.py: SKILL_GEN_FREEFORM/REGEN_FREEFORM_SYSTEM (a 'menu' prompt letting the skill model choose whatever form helps this problem — analysis, concept, pitfall, tiny example, blunt directive, even 'let's think step by step'), wired into style dispatch + --skill-style choices; the freeform prompt carries <skills></skills> wrapper examples so open-form outputs stay parseable - config.py: E21 = bnpo/view-B/freeform (thinking on; see comment for why not off), STYLES + RUN_ORDER updated, self-check passes - trainer.py: freeform shares narrative's 1100-char len budget
The BNPO family returned an already-normalized per-group token-mean with num_tokens=0, so the framework's PER-TOKEN-MEAN path equal-weighted micro/dp groups -> a double average (group token-mean, then equal weight over groups) that sits between token-mean and sequence-mean and biases toward short responses (degrades to pure sequence-mean as groups multiply). This diverged from verl/SEAM's true token-mean and was non-orthogonal to skill-length study. - grpo.py: BNPOLoss gains token_mean_scope='global'(default)|'micro'. 'global' returns the token SUM and reports num_tokens=sum(mask), routing into the framework SUM-loss path -> exact global token-mean, invariant to how the batch is split. 'micro' preserves the old behavior to reproduce E1-E20. Added a _loss_num_tokens hook (default 0) so GRPO/DRGRPO/OPSD are untouched. No public interface change; downstream grad + metric already branch on num_tokens. - tests/loss/test_bnpo_token_mean.py: assert global is split-invariant (==true token-mean), micro reproduces the biased double-average, SEAM inherits global. - run_ablate12.sh: E13(140G)/E21(80G) OOM'd in train forward at micro=8; set per-arm train_micro_batch defaults (E13=2xdp, E21=1xdp). The global token-mean fix makes shrinking micro mathematically equivalent, so effective batch and comparability are unchanged.
E13/E21 blocks referenced $TRAIN_MICRO_BATCH directly; under set -u an unset
env aborts with 'unbound variable'. Use ${TRAIN_MICRO_BATCH:-}.
…kers - sanitize sys.argv around LLMAgent construction: ms-agent's Config.parse_args() mis-parses a foreign argv in forked/Ray workers (assert crash on value tokens; silent flag mispairing otherwise) - tolerate ms-agent >= 1.6.0 API changes: prepare_skills -> _ensure_auto_skills(), dropped ms_agent.hooks and _append_task_notifications, defensive ToolResult field forwarding
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challenger/{base,agentic,recorder}).agents/{base,ms_agent}).harness/*).verifier/{result_check,rubric_score}).condenser/,chunker/,data_format/chunks).rollout/{endpoint,external,ledger,trace}; removesapi_multi_turn/bridge/multi_turn_condense).envs/{localenv,snapshot}).utils/*).loss/opsd).template/tools/bracket_dsl).score_filter/llm_backend).protocol/api_sampler)..base_layer.suffix + fusedin_proj_qkvzmapping).flash_attention_2for padding-free training.Experiment results
Paste your experiment result here(if needed).