Reinforcement learning has advanced large language models on tasks with reliable verifiers, where checking an outcome provides a clear basis for reward. Extending this approach to open-ended tasks requires environments that can both evaluate complex, partially successful outputs and turn these judgments into effective...
Rui Jin, Jikai Chen, Cheng-Yue Yu et al.· 1 citation
A hierarchical credit assignment framework that retains RL's verifier-bounded ceiling while incorporating dense token-level signals from a privileged self-teacher is introduced, unlocking dense credit assignment without sacrificing the verifier-bounded ceiling.
Zechuan Wang, Siyuan Lu, Hongxuan Zhang et al.· 4 citations
An underlying mechanism explaining the gap in uncertainty quantification is identified: path switching, where agents frequently abandon their current search direction in-trajectory, breaking the link between early signal and final outcome.
Zongrun Li, Cheng-Yue Yu, Lei Zang et al.· 2 citations
A reinforcement learning framework that converts rubric-level functional feedback into localized optimization signals over generated code and aligns evaluator-generated textual attributions with responsible code spans and generated tokens is proposed.
Rui Jin, Jikai Chen, Yihang Chen et al.· 0 citations
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