The authors target an exploration problem in agent reinforcement learning: if a model almost never completes a difficult task, successful examples are too scarce to provide a useful training signal.
Intervening During Rollouts Helps Agents Learn Beyond Their Initial Limits
A training pipeline corrects pivotal rollout errors, filters implausible corrections and teaches language-model agents from tasks they rarely solve unaided.
Chinese Tech
Chen Xi · Ding Ruixue · Wang Zhigaoyuan · Zhang Fanrui · Zhang Qiang · Chen Boli · +6 more
Alibaba Token Hub, Alibaba Group · HKUST (Guangzhou) · Amap, Alibaba Group
Research Digest··3 min read
Chen and colleagues introduce Rollout-Time Intervention (RTI), a reinforcement-learning method that turns selected failed agent rollouts into successful training trajectories using evaluation rubrics.
Why this paper
From Alibaba Token Hub, Alibaba Group and 2 others
In one line
Reward-aware correction of failed rollouts at intervention time lets LLM agents learn from trajectories they could not generate on their own, pushing RL beyond policy limits.
What we could check
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