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On Entropy Control in LLM-RL Algorithms

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
reinforcement learningLLM
Abstract:

For RL algorithms, appropriate entropy control is crucial to their effectiveness. To control the policy entropy, a commonly used method is entropy regularization, which is adopted in various popular RL algorithms including PPO, SAC and A3C. Although entropy regularization proves effective in robotic and games RL conventionally, studies found that it gives weak to no gains in LLM-RL training. In this work, we study the issues of entropy bonus in LLM-RL setting. Specifically, we first argue that the conventional entropy regularization suffers from the LLM's extremely large response space and the sparsity of the optimal outputs. As a remedy, we propose AEnt, an entropy control method that utilizes a new clamped entropy bonus with an automatically adjusted coefficient. The clamped entropy is evaluated with the re-normalized policy defined on certain smaller token space, which encourages exploration within a more compact response set. In addition, the algorithm automatically adjusts entropy coefficient according to the clamped entropy value, effectively controlling the entropy-induced bias while leveraging the entropy's benefits. AEnt is tested in math-reasoning tasks under different base models and datasets, and it is observed that AEnt outperforms the baselines consistently across multiple benchmarks.

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Overview

Overall Novelty Assessment

The paper proposes AEnt, an adaptive entropy regularization method for LLM-RL that addresses issues arising from large response spaces and sparse optimal outputs. It resides in the 'Adaptive and Dynamic Entropy Regularization' leaf, which contains four papers including this one. This leaf sits within the broader 'Entropy Regularization Methods and Mechanisms' branch, indicating a moderately populated research direction focused on explicit entropy control techniques. The taxonomy shows fifty papers across the entire field, with this particular leaf representing one of several approaches to entropy management in LLM-RL training.

The taxonomy reveals neighboring leaves addressing related but distinct mechanisms: 'Fixed-Coefficient and Clipping-Based Methods' explores static regularization schemes, 'KL-Divergence Regularization Design' examines divergence-based constraints, and 'Entropy Minimization Approaches' focuses on concentration rather than exploration. The paper's adaptive coefficient adjustment connects it to the broader 'Exploration-Driven Approaches' branch, which includes curiosity-based and uncertainty-guided methods. The scope note for the paper's leaf explicitly excludes fixed-coefficient methods and clipping-based approaches, positioning AEnt as a dynamic alternative to static entropy control strategies.

Among thirty candidates examined, the analysis identified limited prior work overlap. The theoretical analysis of entropy regularization issues in LLM-RL showed no refutable candidates across ten examined papers. The clamped entropy bonus mechanism similarly found no overlapping prior work among ten candidates. The adaptive coefficient adjustment scheme encountered one refutable candidate among ten examined, suggesting some existing work on dynamic entropy tuning. The relatively small number of refutable findings across contributions indicates that, within the examined candidate set, the combination of clamped entropy and automatic coefficient adjustment appears less extensively explored.

Based on the limited search scope of thirty semantically similar papers, the work appears to occupy a moderately novel position within adaptive entropy regularization. The taxonomy structure suggests this is an active but not overcrowded research area, with the paper's specific combination of clamped entropy and automatic coefficient adjustment showing limited overlap in the examined candidate pool. The analysis does not cover exhaustive literature review or assess contributions outside the top-K semantic matches and their citations.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: entropy control in large language model reinforcement learning. The field addresses how to manage the randomness and diversity of token-level decisions when training LLMs with RL, balancing exploration of novel responses against exploitation of high-reward behaviors. The taxonomy organizes research into several main branches: Entropy Regularization Methods and Mechanisms focuses on explicit penalties or bonuses that shape policy entropy, including fixed-coefficient schemes and adaptive strategies that adjust regularization strength during training. Exploration-Driven Approaches emphasize curiosity signals and uncertainty-based mechanisms to guide search in large action spaces. Token-Level and Sample-Level Analysis examines entropy at different granularities, from individual token distributions to full sequence variability. Entropy Collapse and Training Stability investigates pathologies where policies become overly deterministic or unstable, while Policy Optimization Algorithms and Frameworks covers broader algorithmic designs that incorporate entropy considerations. Application Domains and Specialized Settings explores how entropy control manifests in reasoning tasks, code generation, and other specialized contexts. Representative works such as EPO Entropy Regularized[3] and ETTRL Entropy Mechanism[2] illustrate how regularization can be integrated into policy gradient methods, while Efficiency Exploration RL[5] and Reasoning Exploration Entropy[7] highlight exploration-centric designs. A particularly active line of work centers on adaptive and dynamic entropy regularization, where the strength or form of entropy penalties evolves based on training signals or task characteristics. Entropy Control LLM-RL[0] sits squarely within this adaptive branch, proposing mechanisms that adjust regularization dynamically rather than relying on fixed hyperparameters. This contrasts with neighboring efforts like Adaptive Divergence Regularization[43], which modulates KL penalties between policy and reference distributions, and Adaptive Entropy Coefficient[45], which tunes a scalar entropy weight over time. The central trade-off across these methods is between maintaining sufficient exploration to discover high-quality solutions and preventing entropy collapse that leads to degenerate or repetitive outputs. Open questions include how to set or learn adaptation schedules, whether token-level or sequence-level entropy metrics are more informative, and how entropy control interacts with reward shaping and other training stabilizers in large-scale LLM settings.

Claimed Contributions

Theoretical analysis of entropy regularization issues in LLM-RL

The authors provide a theoretical framework explaining why traditional entropy regularization fails in LLM-RL settings. They show that entropy collapse indicates learning stagnancy and that conventional entropy regularization suffers from bias due to LLM's large response space and sparse optimal actions, as formalized in Propositions 1 and 2.

10 retrieved papers
AEnt algorithm with clamped entropy bonus

The authors introduce AEnt, a novel entropy regularization method that computes entropy on a re-normalized policy defined over a reduced token space (top probability tokens). This clamped entropy encourages exploration within a more compact response set, reducing the bias induced by the extremely large vocabulary in LLMs.

10 retrieved papers
Adaptive entropy coefficient adjustment scheme

The authors propose an automatic adjustment mechanism for the entropy coefficient during training. The coefficient is dynamically updated to keep the clamped entropy within specified bounds, balancing the benefits of entropy regularization against its bias and preventing issues like entropy collapse or explosion.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Theoretical analysis of entropy regularization issues in LLM-RL

The authors provide a theoretical framework explaining why traditional entropy regularization fails in LLM-RL settings. They show that entropy collapse indicates learning stagnancy and that conventional entropy regularization suffers from bias due to LLM's large response space and sparse optimal actions, as formalized in Propositions 1 and 2.

Contribution

AEnt algorithm with clamped entropy bonus

The authors introduce AEnt, a novel entropy regularization method that computes entropy on a re-normalized policy defined over a reduced token space (top probability tokens). This clamped entropy encourages exploration within a more compact response set, reducing the bias induced by the extremely large vocabulary in LLMs.

Contribution

Adaptive entropy coefficient adjustment scheme

The authors propose an automatic adjustment mechanism for the entropy coefficient during training. The coefficient is dynamically updated to keep the clamped entropy within specified bounds, balancing the benefits of entropy regularization against its bias and preventing issues like entropy collapse or explosion.

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