# Enhancing Efficiency and Exploration in Reinforcement Learning for LLMs

arXiv: https://arxiv.org/abs/2505.18573 · HTML: https://mlsanity.com/paper/2505.18573/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning.
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 24 May 2025.

## Placed in

- LLM reasoning / [Reinforcement learning for reasoning](https://mlsanity.com/lr/reinforcement-learning-for-reasoning/)

## Builds on (most cited first, 8 of 8)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [HybridFlow: A Flexible and Efficient RLHF Framework](https://mlsanity.com/paper/2409.19256/) (2024)
- [Understanding R1-Zero-Like Training: A Critical Perspective](https://mlsanity.com/paper/2503.20783/) (2025)
- [OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems](https://mlsanity.com/paper/2402.14008/) (2024)
- [Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?](https://mlsanity.com/paper/2504.13837/) (2025)

## Built on by (most cited first, 23 of 23)

- [A Survey of Reinforcement Learning for Large Reasoning Models](https://mlsanity.com/paper/2509.08827/) (2025)
- [SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning](https://mlsanity.com/paper/2509.09674/) (2025)
- [Improving Sampling Efficiency in RLVR through Adaptive Rollout and Response Reuse](https://mlsanity.com/paper/2509.25808/) (2025)
- [Decomposing the Entropy-Performance Exchange: The Missing Keys to Unlocking Effective Reinforcement Learning](https://mlsanity.com/paper/2508.02260/) (2025)
- [XRPO: Pushing the limits of GRPO with Targeted Exploration and Exploitation](https://mlsanity.com/paper/2510.06672/) (2025)
- [Adaptive Rollout Allocation for Online Reinforcement Learning with Verifiable Rewards](https://mlsanity.com/paper/2602.01601/) (2026)
- [Train Less, Learn More: Adaptive Efficient Rollout Optimization for Group-Based Reinforcement Learning](https://mlsanity.com/paper/2602.14338/) (2026)
- [Uniform-Correct Policy Optimization: Breaking RLVR's Indifference to Diversity](https://mlsanity.com/paper/2605.00365/) (2026)
- [On the Entropy Dynamics in Reinforcement Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2602.03392/) (2026)
- [G-KV: Decoding-Time KV Cache Eviction with Global Attention](https://mlsanity.com/paper/2512.00504/) (2025)
- [Efficient Reinforcement Learning with Semantic and Token Entropy for LLM Reasoning](https://mlsanity.com/paper/2512.04359/) (2025)
- [Thinking with Deltas: Incentivizing Reinforcement Learning via Differential Visual Reasoning Policy](https://mlsanity.com/paper/2601.06801/) (2026)
- [RISER: Orchestrating Latent Reasoning Skills for Adaptive Activation Steering](https://mlsanity.com/paper/2601.09269/) (2026)
- [Offline Exploration-Aware Fine-Tuning for Long-Chain Mathematical Reasoning](https://mlsanity.com/paper/2603.16206/) (2026)
- [Difficulty-Adaptive Tree-Structured Policy Optimization for Expanding Reasoning Coverage in RLVR](https://mlsanity.com/paper/2609.08650/) (2026)
- [Nonsense Helps: Prompt Space Perturbation Broadens Reasoning Exploration](https://mlsanity.com/paper/2605.05566/) (2026)
- [Adaptive Nucleus Truncation for Long-Form Reasoning](https://mlsanity.com/paper/2606.13982/) (2026)
- [AIPO: Learning to Reason from Active Interaction](https://mlsanity.com/paper/2605.08401/) (2026)
- [Epistemic Uncertainty for Test-Time Discovery](https://mlsanity.com/paper/2605.11328/) (2026)
- [When to Stop Reusing: Dynamic Gradient Gating for Sample-Efficient RLVR](https://mlsanity.com/paper/2605.19425/) (2026)
- [HiFloat4 Format for End-To-End Reinforcement Learning Post-Training of Large Language Models](https://mlsanity.com/paper/2607.26515/) (2026)
- [Learning from Hard Prompts: Difficulty-aware Advantage Amplification in Dynamic Sampling](https://mlsanity.com/paper/2608.27982/) (2026)
- [SERA: Scale-Equalized Rollout Allocation for Maximum Likelihood Reinforcement Learning](https://mlsanity.com/paper/2609.36552/) (2026)

## Abstract (arXiv)

Reasoning large language models (LLMs) excel in complex tasks, which has drawn significant attention to reinforcement learning (RL) for LLMs. However, existing approaches allocate an equal number of rollouts to all questions during the RL process, which is inefficient. This inefficiency stems from the fact that training on simple questions yields limited gains, whereas more rollouts are needed for challenging questions to sample correct answers. Furthermore, while RL improves response precision, it limits the model's exploration ability, potentially resulting in a performance cap below that of the base model prior to RL. To address these issues, we propose a mechanism for dynamically allocating rollout budgets based on the difficulty of the problems, enabling more efficient RL training. Additionally, we introduce an adaptive dynamic temperature adjustment strategy to maintain the entropy at a stable level, thereby encouraging sufficient exploration. This enables LLMs to improve response precision while preserving their exploratory ability to uncover potential correct pathways. The code and data is available on: https://github.com/LiaoMengqi/E3-RL4LLMs