Source-linked AI summary
Entropy-guided sequence weighting for efficient exploration in RL-based LLM fine-tuning
Abdullah Vanlioglu
TL;DR
LLM reasoning remains challenging, and RL fine-tuning requires efficient exploration in high-dimensional state spaces. EGSW weights generated outputs using advantage and entropy, and the paper reports improved GRPO reasoning and sample efficiency. Its stated scope includes step-wise and trajectory-wise settings and possible use with other RL algorithms.
Problem
Complex LLM reasoning remains challenging, while RL fine-tuning in high-dimensional state spaces makes efficient exploration important.
Source: §1 · p. 1Method
EGSW combines advantage- and entropy-based sequence weights, using temperature-scaled softmax to prioritize high-reward, high-uncertainty outputs.
Source: §1 · pp. 1–2Results
Takeaways & Limitations
EGSW is presented as applicable in both step-wise and trajectory-wise RL frameworks, with potential extensions to other RL algorithms.
Source: §1 · p. 2EGSW is sensitive to hyperparameter tuning, particularly entropy scaling factor α and temperature P; improper tuning may cause excessive exploration and suboptimal performance.
Source: §6 · p. 7
Abstract
from arXiv · showhide
We introduce Entropy-Guided Sequence Weighting (EGSW), a novel approach that enhances the exploration-exploitation tradeoff by dynamically assigning weights to generated outputs based on their advantage and entropy for Reinforcement Learning-based Large Language Model fine-tuning. EGSW integrates entropy regularization with advantage-based weighting to balance policy updates, enabling efficient exploration in high-dimensional state spaces. By employing temperature-scaled softmax weighting over sequences, EGSW prioritizing high-reward, high-uncertainty steps while maintaining training stability. Although originally developed to improve Group Relative Policy Optimization (GRPO) during large language model (LLM) fine-tuning, EGSW is generalizable to other reinforcement learning (RL) algorithms and can be implemented in both step-wise and trajectory-wise settings. Empirical evaluations demonstrate that EGSW enhances GRPO reasoning ability, yielding improvements in sample efficiency. Future work will explore the application of EGSW to advanced RL methodologies.
1 Introduction
LLM reasoning remains challenging, and RL fine-tuning faces inefficient exploration in high-dimensional state spaces. EGSW weights generated sequences using advantage and entropy to improve exploration efficiency while maintaining computational feasibility.
1 Introduction
- LLM fine-tuning with RL treats reasoning as sequential decisions refined through reward feedback, but efficient exploration remains difficult in high-dimensional state spaces.The reward may come from an external verifier or learned reward model.Source: §1 · p. 1
- GRPO relies on policy-driven sampling, which can explore inefficiently, while MCTS and inference-time search impose computational cost or latency.MCTS has succeeded in structured domains, but its computational cost limits its practicality for LLM fine-tuning.Source: §1 · p. 1
- EGSW addresses inefficient exploration in high-dimensional RL fine-tuning by dynamically weighting generated sequences using entropy and advantage.Its temperature-scaled softmax prioritizes sequences with both high reward and high uncertainty.Source: §1 · pp. 1–2
- EGSW combines entropy with advantage-based weighting to enhance reasoning performance across MATH-500 [5], AIME, and GPQA Diamond.The introduction also presents EGSW as an efficient alternative to computationally expensive search-based methods.Source: §1 · p. 2
- EGSW can be applied in step-wise or trajectory-wise RL frameworks, with potential extensions to other advanced RL algorithms.The stated aim is to enhance LLM reasoning while maintaining practical computational constraints.Source: §1 · p. 2
2 Related Work
Related work explores search-based, entropy-regularized, replay-based, and search-free approaches to exploration in RL and LLM fine-tuning. The cited work motivates a lightweight approach that prioritizes informative, high-reward reasoning steps.
2 Related Work
- MCTS supports systematic exploration in games and has been adapted to LLM reasoning, but its computational demands limit large-scale fine-tuning and inference-time use adds latency and overhead.The passage cites LLM applications in code world models [6] and iterative preference learning [7].Source: §2 · p. 2
- Entropy regularization promotes stochastic policies, but naïve entropy maximization can add randomness without prioritizing reasoning steps that are both informative and high-reward.The passage discusses entropy-regularized RL [8] and Soft Actor-Critic [9].Source: §2 · p. 2
- Prioritized Trajectory Replay and PTR-PPO prioritize past trajectories, but the described methods do not explicitly incorporate entropy regularization.The cited approaches are PTR [10] and PTR-PPO [11].Source: §2 · p. 2
- GRPO stabilizes training with group-normalized rewards and per-token advantages, but may underuse trajectory-level uncertainty in high-dimensional exploration.The passage describes DeepSeek V3 [12] as using GRPO for search-free LLM fine-tuning.Source: §2 · p. 2
- Other cited work examines critical-token exploration [13] and fine-tuning for inference-time Best-of-N sampling [14].The latter addresses the non-differentiable nature of some selection processes.Source: §2 · p. 3
3 Background
GRPO extends PPO by normalizing advantage estimates across groups of sampled outputs to stabilize policy updates. Its described framework also includes a likelihood ratio, a KL-divergence penalty, and a group-based normalization of advantages.
3 Background
- GRPO extends PPO and stabilizes policy updates by normalizing advantage estimates over groups of samples.The stated purpose is to reduce variance within groups of trajectories.Source: §3.1 · p. 3
- The background introduces a standard PPO objective and a likelihood ratio for a question and output, with A_t denoting the advantage at time step t.The GRPO objective is described as combining the framework's components.Source: §3.1 · p. 3
- For each question, GRPO samples a group of outputs and normalizes each step's advantage using group reward statistics.The mean and standard deviation of rewards in each group are used for normalization.Source: §3.1 · p. 3
- GRPO includes a KL-divergence penalty to constrain updates and limit deviation of the new policy from the old policy.The coefficient β determines the penalty's strength.Source: §3.1 · p. 3
4 Entropy-Guided Sequence Weighting (EGSW)
EGSW integrates entropy with advantage-based sequence and step weighting to direct policy updates toward high-reward, high-uncertainty outputs. It computes step or trajectory entropy, normalizes weights within a batch, and uses them to reweight policy gradients while preserving a similar gradient scale.
4 Entropy-Guided Sequence Weighting (EGSW)
- EGSW dynamically weights generated sequences or individual reasoning steps by advantage and entropy, steering updates toward high-reward, high-uncertainty outputs.Source: §4 · p. 3
- For each reasoning step, EGSW computes raw weights from advantage and entropy; α scales entropy, while temperature P controls weight-distribution sparsity.Source: §4 · pp. 3–4
- Step entropy sums entropies of generated actions, and trajectory-level entropy sums across reasoning steps.Source: §4 · p. 4
- A batch softmax normalizes sequence weights, which reweight policy gradients and keep overall gradient scale similar to standard updates.Source: §4 · p. 4
5 Experiments
Experiments fine-tune Qwen2.5-Math-7B and Qwen2.5-Math-7B-Instruct with GRPO and EGSW, comparing training rewards, completion lengths, and benchmark evaluations. EGSW is reported to improve reward scores, including for the Instruct model, while using fewer tokens for higher rewards.
Experiments
- For Qwen2.5-Math-7B, GRPO with EGSW achieved better training scores than GRPO.Figure 1 shows the training trends for the two methods.Source: §5 · p. 5, Figure 1 · p. 5
- EGSW consistently outperformed standard GRPO in reward scores for Qwen2.5-Math-7B-Instruct during training.Figure 3 presents the compared training reward trends.Source: §5 · p. 5, Figure 3 · p. 6
- Fine-tuning Qwen2.5-Math-7B-Instruct with GRPO on the 8K MATH dataset alone did not improve reasoning, whereas adding EGSW enhanced reasoning ability.The authors attribute this improvement to EGSW encouraging better exploration.Source: §5 · p. 6
- The best reported settings used normalized entropy: α = 0.3 and P = 1 for Qwen2.5-Math-7B, and α = 0.8 and P = 1.8 for its Instruct variant.Evaluation scores cover Math-500, AIME24, AIME25, and GPQA Diamond, averaged over three seeds.Source: Table 1 · p. 5, §5 · p. 6, Table 2 · p. 7
- High entropy coefficients can drive excessive exploration toward high-entropy trajectories and result in suboptimal performance.The method is also described as sensitive to hyperparameter tuning; its lower overall gradient norm may require learning-rate adjustment for stable learning.Source: §5 · pp. 6–7
- EGSW is reported to enable fewer generated tokens while achieving higher rewards.Figures 2 and 4 show completion-length trends for the two model settings.Source: Figure 2 · p. 5, §5 · p. 6, Figure 4 · p. 6
6 Conclusion
EGSW dynamically weights sequences using advantage and entropy to improve GRPO’s exploration-exploitation balance, training stability, and efficiency. Experiments report higher reward scores and improved reasoning, while the method is sensitive to hyperparameter tuning.
6 Conclusion
- EGSW dynamically adjusts sequence weights using advantage and entropy to encourage diverse, informative trajectories while focusing on high-reward outputs.The authors report that this approach improves training stability and efficiency.Source: §6 · p. 7
- Experiments on Qwen2.5-Math-7B and Qwen2.5-Math-7B-Instruct found that EGSW-enhanced GRPO achieved higher reward scores and improved reasoning capabilities.Source: §6 · p. 7
- EGSW is highly sensitive to hyperparameter tuning, particularly the entropy scaling factor α and temperature P.Improper tuning may lead to excessive exploration and suboptimal performance; future work will examine integration with other RL fine-tuning strategies.Source: §6 · p. 7