Experience is the Best Teacher: Motivating Effective Exploration in Reinforcement Learning for LLMs
Reinforcement Learning (RL) with rubric-based rewards has recently shown remarkable progress in enhancing general reasoning capabilities of Large Language Models (LLMs), yet still suffers from ineffective exploration confined to curent policy distribution. In fact, RL optimization can be viewed as steering the policy toward an ideal distribution that maximizes the rewards, while effective exploration should align efforts with desired target. Leveraging this insight, we propose HeRL, a Hindsight experience guided Reinforcement Learning framework to bootstrap effective exploration by explicitly telling LLMs the desired behaviors specified in rewards. Concretely, HeRL treats failed trajector...
| Authors | Wenjian Zhang, Kongcheng Zhang, Jiaxin Qi et al. |
|---|---|
| Primary category | cs.AI |
| Published | 20 March 2026 |
| Last updated at source | 20 March 2026 |
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