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

• URL: http://arxiv.org/abs/2603.20046v1
• Date: Fri, 20 Mar 2026 15:32:06 GMT
• Title: Experience is the Best Teacher: Motivating Effective Exploration in Reinforcement Learning for LLMs
• Abstract summary: HeRL is a Hindsight experience guided Reinforcement Learning framework.<n>We show how HeRL bootstraps effective exploration by explicitly telling LLMs the desired behaviors specified in rewards.
• Score: 17.47321135631444
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• LLM-as-a-Coach: Experiential Learning for Non-Verifiable Tasks [89.13372484640392]
  We propose Experiential Learning (EL), which repurposes the feedback model from an LLM-as-a-Judge into an LLM-as-a-Coach.<n>EL consistently outperforms rubric-based RL on held-out and unseen open-ended tasks.<n>These findings establish experiential knowledge as a richer and more generalizable learning signal for post-training on non-verifiable tasks.
  arXiv  Detail & Related papers  (2026-07-20T16:08:49Z)
• SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models [51.99912169291891]
  Reinforcement learning (RL) has emerged as a promising paradigm for training reasoning-oriented models.<n>We propose a training paradigm that interleaves conventional RL with inverse reinforcement learning (IRL)<n>Our findings suggest that alternating between RL and IRL offers an effective pathway toward extending the exploration capacity of reasoning-oriented large language models.
  arXiv  Detail & Related papers  (2026-04-18T13:49:47Z)
• Diversity or Precision? A Deep Dive into Next Token Prediction [19.30494719444709]
  We study how the pre-trained token-output distribution shapes the exploration potential for subsequent reinforcement learning.<n>We find that imposing a precision-oriented gradient prior yields a superior exploration space for RL.
  arXiv  Detail & Related papers  (2025-12-28T14:53:24Z)
• Attention as a Compass: Efficient Exploration for Process-Supervised RL in Reasoning Models [47.05227816684691]
  We introduce a novel PSRL framework (AttnRL) which enables efficient exploration for reasoning models.<n>Motivated by preliminary observations that steps exhibiting high attention scores correlate with reasoning behaviors, we propose to branch from positions with high values.<n>We develop an adaptive sampling strategy that accounts for problem difficulty and historical batch size, ensuring that the whole training batch maintains non-zero advantage values.
  arXiv  Detail & Related papers  (2025-09-30T17:58:34Z)
• Reinforcement Learning on Pre-Training Data [55.570379963147424]
  We introduce Reinforcement Learning on Pre-Training data (R), a new training-time scaling paradigm for optimizing large language models (LLMs)<n>R enables the policy to autonomously explore meaningful trajectories to learn from pre-training data and improve its capability through reinforcement learning (RL)<n>Extensive experiments on both general-domain and mathematical reasoning benchmarks across multiple models validate the effectiveness of R.
  arXiv  Detail & Related papers  (2025-09-23T17:10:40Z)
• Agentic Reinforcement Learning with Implicit Step Rewards [92.26560379363492]
  Large language models (LLMs) are increasingly developed as autonomous agents using reinforcement learning (agentic RL)<n>We introduce implicit step rewards for agentic RL (iStar), a general credit-assignment strategy that integrates seamlessly with standard RL algorithms.<n>We evaluate our method on three challenging agent benchmarks, including WebShop and VisualSokoban, as well as open-ended social interactions with unverifiable rewards in SOTOPIA.
  arXiv  Detail & Related papers  (2025-09-23T16:15:42Z)
• Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM Reasoning [87.7836502955847]
  We propose a novel self-rewarding reinforcement learning framework to enhance Large Language Model (LLM) reasoning.<n>Our key insight is that correct responses often exhibit consistent trajectory patterns in terms of model likelihood.<n>We introduce CoVo, an intrinsic reward mechanism that integrates Consistency and Volatility via a robust vector-space aggregation strategy.
  arXiv  Detail & Related papers  (2025-06-10T12:40:39Z)
• Beyond Markovian: Reflective Exploration via Bayes-Adaptive RL for LLM Reasoning [55.36978389831446]
  We recast reflective exploration within the Bayes-Adaptive RL framework.<n>Our resulting algorithm, BARL, instructs the LLM to stitch and switch strategies based on observed outcomes.
  arXiv  Detail & Related papers  (2025-05-26T22:51:00Z)
• Preference-Guided Reinforcement Learning for Efficient Exploration [14.058764537783086]
  We introduce LOPE: textbfLearning textbfOnline with trajectory textbfPreferencedanctextbfE, an end-to-end preference-guided RL framework.<n>Our intuition is that LOPE directly adjusts the focus of online exploration by considering human feedback as guidance.<n>LOPE outperforms several state-of-the-art methods in terms of convergence rate and overall performance.
  arXiv  Detail & Related papers  (2024-07-09T02:11:12Z)
• Information Directed Reward Learning for Reinforcement Learning [64.33774245655401]
  We learn a model of the reward function that allows standard RL algorithms to achieve high expected return with as few expert queries as possible. In contrast to prior active reward learning methods designed for specific types of queries, IDRL naturally accommodates different query types. We support our findings with extensive evaluations in multiple environments and with different types of queries.
  arXiv  Detail & Related papers  (2021-02-24T18:46:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.