Think Twice Before You Write — an Entropy-based Decoding Strategy to Enhance LLM Reasoning
Summary: 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.
Introduction
Large language models (LLMs) have revolutionized natural language processing, but their reasoning capabilities are often limited by the decoding strategies used during generation. Traditional methods like greedy decoding and beam search are prone to error propagation, which can lead to suboptimal outcomes. In contrast, sampling-based approaches add a layer of randomness, yet they lack the necessary robustness to ensure reliability.
The Entropy-Guided Decoding Framework
In response to the limitations of existing strategies, we introduce an entropy-guided decoding framework aimed at enhancing the reasoning abilities of LLMs. This innovative approach integrates token-level adaptivity into the generation process by employing the following techniques:
- Entropy Computation: At each generation step, the model calculates the entropy of the token distribution. This computation helps in identifying high-uncertainty positions within the output.
- Selective Branching: By focusing on these vulnerable points, the model selectively branches to explore more promising paths in its reasoning.
- Dynamic Rollout Pool: A dynamic pool of partial rollouts is maintained and expanded, which concentrates computational resources where uncertainty is greatest while avoiding unnecessary exploration in areas of high confidence.
- Efficient Termination: To facilitate efficient stopping, we implement the rollout-level Entropy After (EAT) stopping criterion, which evaluates entropy after the full reasoning trace rather than incrementally at each step.
Experimental Results
To evaluate the effectiveness of our entropy-guided decoding framework, we conducted experiments on various benchmarks, including GSM8K and AMC2023, along with their perturbed variants. Our findings reveal that:
- Our method consistently achieves strong accuracy across all tested datasets.
- Notably, the performance of smaller LLMs using our framework is comparable to larger models like GPT-5.
- Additionally, our approach operates at a fraction of the computational cost associated with traditional methods.
Conclusion
The entropy-guided decoding strategy presents a significant advancement in enhancing the reasoning capabilities of large language models. By introducing token-level adaptivity and focusing computational resources on areas of uncertainty, our method not only improves accuracy but also offers a more efficient alternative to traditional decoding strategies. As LLMs continue to evolve, our framework could serve as a foundation for future developments in robust and reliable natural language generation.