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
InterTab is a structure-aware framework for CoT reasoning over table images that interleaves chain-of-thought with tool calls that crop structure-aligned table regions, and improves the average accuracy of its backbone from 68.28% to 73.17% and achieves the best average performance among all compared methods.
Hanqian Li, Si-Rui Huang, Chen Ling et al.· 0 citations
MetaRAG is proposed, a belief-action aligned policy optimization framework for agentic RAG that consistently improves the accuracy-efficiency trade-off over strong RL-based agentic RAG baselines, with gains that transfer to deep research settings, different optimizers, and multiple model backbones.
Qiuyi Qi, Tian Liang, Jiamu Wang et al.· 0 citations
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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