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cs.LG2026
Reasoning Cache: Continual Improvement Over Long Horizons via Short-Horizon RL
Ian Wu, Yuxiao Qu, Amrith Setlur +1
Large Language Models (LLMs) that can continually improve beyond their training budgets are able to solve increasingly difficult problems by adapting at test time, a property we re…
cs.LG2026
InT: Self-Proposed Interventions Enable Credit Assignment in LLM Reasoning
Matthew Y. R. Yang, Hao Bai, Ian Wu +3
Outcome-reward reinforcement learning (RL) has proven effective at improving the reasoning capabilities of large language models (LLMs). However, standard RL assigns credit only at…