New Research Proposes Greedy Pruning Method to Compress LLM Reasoning Chains
A recent study presents greedy pruning, a diagnostic technique designed to compress reasoning chains in large language models. This method fills a gap in the comprehension of whether these models encode the functional significance of tokens for generating answers. Previous methods for shortening reasoning chains have depended on probabilistic sampling, heuristics, or guidance from advanced models, providing limited understanding of internal encoding. Greedy pruning employs a likelihood-preserving deletion process that systematically eliminates reasoning tokens with minimal impact on model likelihood based on a defined objective, resulting in controlled-length reasoning chains. Evaluating pruned reasoning within a distillation framework, the findings indicate that students trained on these pruned chains surpass a baseline that uses frontier model supervision at equivalent reasoning lengths. The paper can be accessed on arXiv under identifier 2601.03066v3.
Key facts
- The paper introduces a method called greedy pruning for compressing reasoning chains in LLMs.
- Greedy pruning is a likelihood-preserving deletion procedure.
- It iteratively removes reasoning tokens that minimally degrade model likelihood.
- The method yields length-controlled reasoning chains.
- It addresses whether models internally encode token-level functional importance for answer generation.
- Prior work used probabilistic sampling, heuristics, or supervision from frontier models.
- In a distillation framework, students trained on pruned chains outperformed a frontier-model-supervised baseline.
- The paper is available on arXiv with the identifier arXiv:2601.03066v3.
Entities
Institutions
- arXiv