Academic

Experience is the Best Teacher: Motivating Effective Exploration in Reinforcement Learning for LLMs

arXiv:2603.20046v1 Announce Type: new Abstract: 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 trajectories along with their unmet rubrics as hindsight experience, which serves as in-context guidance for the policy to explore desired responses beyond its current distribution. Additionally, we introduce a bonus reward to incentivize responses with greater p

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Wenjian Zhang, Kongcheng Zhang, Jiaxin Qi, Baisheng Lai, Jianqiang Huang · · 1 min read · 9 views
#cs.AI

arXiv:2603.20046v1 Announce Type: new Abstract: 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 trajectories along with their unmet rubrics as hindsight experience, which serves as in-context guidance for the policy to explore desired responses beyond its current distribution. Additionally, we introduce a bonus reward to incentivize responses with greater potential for improvement under such guidance. HeRL facilitates effective learning from desired high quality samples without repeated trial-and-error from scratch, yielding a more accurate estimation of the expected gradient theoretically. Extensive experiments across various benchmarks demonstrate that HeRL achieves superior performance gains over baselines, and can further benefit from experience guided self-improvement at test time. Our code is available at https://github.com/sikelifei/HeRL.

Executive Summary

This article proposes a novel reinforcement learning framework, HeRL, to enhance effective exploration in large language models (LLMs) by incorporating hindsight experience and bonus rewards. HeRL leverages failed trajectories and unmet rubrics as hindsight experience, guiding the policy to explore desired responses beyond its current distribution. Extensive experiments demonstrate superior performance gains over baselines, with potential benefits from experience-guided self-improvement at test time. The framework aims to optimize LLMs' general reasoning capabilities by aligning exploration efforts with desired targets. The proposed method shows promise in addressing the exploration-exploitation trade-off in RL, particularly in the context of LLMs.

Key Points

  • ▸ HeRL framework incorporates hindsight experience to guide effective exploration in LLMs.
  • ▸ Failed trajectories and unmet rubrics serve as in-context guidance for the policy to explore desired responses.
  • ▸ Bonus rewards incentivize responses with greater potential for improvement under hindsight experience guidance.

Merits

Strength in Addressing Exploration-Exploitation Trade-off

HeRL effectively addresses the exploration-exploitation trade-off by leveraging hindsight experience to guide the policy towards desired targets, enabling more accurate estimation of the expected gradient.

Improved General Reasoning Capabilities

HeRL optimizes LLMs' general reasoning capabilities by aligning exploration efforts with desired targets, leading to superior performance gains over baselines.

Demerits

Limited Evaluation on Real-World Applications

The article primarily focuses on benchmark experiments, and it is unclear how HeRL would perform in real-world applications, where complexity and uncertainty are higher.

Dependence on Quality of Hindsight Experience

The effectiveness of HeRL relies heavily on the quality of hindsight experience, which may not always be available or reliable, potentially limiting its applicability.

Expert Commentary

The proposed HeRL framework is a significant contribution to the field of reinforcement learning, particularly in the context of large language models. By leveraging hindsight experience and bonus rewards, HeRL effectively addresses the exploration-exploitation trade-off, enabling more accurate estimation of the expected gradient. However, the article's focus on benchmark experiments raises questions about the framework's performance in real-world applications, where complexity and uncertainty are higher. Furthermore, the dependence on quality hindsight experience may limit the applicability of HeRL. Nonetheless, the proposed method shows promise in optimizing LLMs' general reasoning capabilities and addressing the exploration-exploitation trade-off in RL.

Recommendations

  • ✓ Future research should focus on evaluating HeRL in real-world applications to assess its performance and limitations in more complex and uncertain environments.
  • ✓ The development of more robust and reliable methods for generating hindsight experience is essential to ensure the effectiveness of HeRL in various scenarios.

Sources

Original: arXiv - cs.AI

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