Think Twice Before You Write -- an Entropy-based Decoding Strategy to Enhance LLM Reasoning
arXiv:2604.00018v1 Announce Type: cross Abstract: Decoding strategies play a central role in shaping the reasoning ability of large language models (LLMs). Traditional methods such as greedy decoding and beam search often suffer from error propagation, while sampling-based approaches introduce randomness without adequate robustness. Self-consistency improves reliability by aggregating multiple rollouts, but incurs significant computational overhead. We propose an entropy-guided decoding framework that introduces token-level adaptivity into generation. At each step, the model computes the entropy of the token distribution, identifies high-uncertainty positions, and selectively branches on these vulnerable points. A dynamic pool of partial rollouts is maintained and expanded until solutions are completed, concentrating computation where uncertainty is greatest and avoiding unnecessary exploration in confident regions. To enable efficient termination, we apply a rollout-level Entropy Aft
arXiv:2604.00018v1 Announce Type: cross Abstract: Decoding strategies play a central role in shaping the reasoning ability of large language models (LLMs). Traditional methods such as greedy decoding and beam search often suffer from error propagation, while sampling-based approaches introduce randomness without adequate robustness. Self-consistency improves reliability by aggregating multiple rollouts, but incurs significant computational overhead. We propose an entropy-guided decoding framework that introduces token-level adaptivity into generation. At each step, the model computes the entropy of the token distribution, identifies high-uncertainty positions, and selectively branches on these vulnerable points. A dynamic pool of partial rollouts is maintained and expanded until solutions are completed, concentrating computation where uncertainty is greatest and avoiding unnecessary exploration in confident regions. To enable efficient termination, we apply a rollout-level Entropy After (EAT) stopping criterion by performing entropy evaluation after the full reasoning trace, rather than incrementally at every step. Experiments on GSM8K, AMC2023, and their perturbed variants demonstrate that our method achieves consistently strong accuracy. Notably, on smaller LLMs, performance is comparable to GPT-5 while operating at a fraction of the cost.
Executive Summary
The article introduces an entropy-guided decoding strategy for large language models (LLMs) that enhances reasoning by dynamically adapting computation based on token-level uncertainty. Unlike traditional methods like greedy decoding or beam search, which suffer from error propagation, or sampling-based approaches, which introduce randomness without robustness, this framework selectively branches on high-uncertainty tokens while maintaining a dynamic pool of partial rollouts. A novel Entropy After (EAT) stopping criterion further improves efficiency by evaluating entropy post-generation rather than incrementally. Experimental results on datasets like GSM8K and AMC2023 demonstrate strong accuracy, even surpassing larger models like GPT-5 at a fraction of the cost. The approach offers a promising balance between computational efficiency and reasoning reliability.
Key Points
- ▸ Introduces an entropy-guided decoding framework that adapts computation based on token-level uncertainty, selectively branching on high-uncertainty tokens to enhance reasoning.
- ▸ Proposes a dynamic pool of partial rollouts and an Entropy After (EAT) stopping criterion to improve efficiency and reduce computational overhead.
- ▸ Demonstrates strong performance on GSM8K, AMC2023, and their perturbed variants, achieving accuracy comparable to GPT-5 with significantly lower computational costs.
Merits
Innovative Adaptivity
The entropy-guided approach dynamically allocates computational resources to high-uncertainty tokens, addressing the limitations of static decoding strategies like greedy or beam search.
Efficiency Gains
The EAT stopping criterion reduces unnecessary incremental evaluations, lowering computational costs while maintaining accuracy.
Robustness in Performance
Experimental results show consistent accuracy improvements across datasets, including perturbed variants, highlighting the method's reliability.
Demerits
Computational Overhead of Dynamic Pooling
Maintaining a dynamic pool of partial rollouts may introduce additional memory and processing overhead, particularly for large-scale models.
Dependency on Entropy Metrics
The method's effectiveness relies on the quality of entropy calculations, which may be influenced by model calibration and token distribution biases.
Limited Generalizability
The study focuses on specific datasets (GSM8K, AMC2023), and further validation across diverse domains is needed to assess broader applicability.
Expert Commentary
This paper presents a compelling advancement in LLM decoding strategies by introducing a principled, uncertainty-driven approach to computational allocation. The entropy-guided framework addresses a critical gap in existing methods, which often struggle with either error propagation (e.g., beam search) or randomness without robustness (e.g., sampling-based methods). The dynamic pool of partial rollouts and the EAT stopping criterion are particularly innovative, as they balance efficiency and accuracy without relying on computationally expensive techniques like self-consistency. However, the method's dependence on entropy metrics raises questions about its sensitivity to model calibration and potential vulnerabilities in adversarial settings. Further, while the results on GSM8K and AMC2023 are impressive, broader validation across diverse tasks—such as code generation or multimodal reasoning—would solidify its generality. Nonetheless, this work sets a strong precedent for adaptive decoding strategies and could pave the way for more efficient and interpretable LLM reasoning systems.
Recommendations
- ✓ Conduct additional experiments to validate the method's robustness across diverse datasets and tasks, including adversarial benchmarks, to assess generalizability.
- ✓ Explore hybrid decoding strategies that integrate entropy-guided branching with other uncertainty-aware techniques (e.g., Monte Carlo Tree Search) to further enhance performance.
- ✓ Investigate the interpretability benefits of entropy-based decoding, such as using token-level uncertainty as a proxy for explainability in high-stakes applications.
Sources
Original: arXiv - cs.AI
