Learning to Reason Efficiently with A* Post-Training
A recent study explores the use of A* search algorithms to improve reasoning in large language models (LLMs). The research indicates that Llama-3.2 models significantly enhance their accuracy and efficiency when trained with A* post-training techniques. The findings suggest a promising approach to developing more reliable deductive reasoning capabilities in AI systems.
- ▪The study investigates how LLMs can learn to generate correct proofs using A* search algorithms.
- ▪Llama-3.2 models improved from near-zero accuracy to outperforming larger models after A* post-training.
- ▪A*-informed signals provide a balance between accuracy and efficiency in reasoning tasks.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.24597 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
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Computer Science > Artificial Intelligence arXiv:2605.24597 (cs) [Submitted on 23 May 2026] Title:Learning to Reason Efficiently with A* Post-Training Authors:Andreas Opedal, Francesco Ignazio Re, Abulhair Saparov, Mrinmaya Sachan, Bernhard Schölkopf, Ryan Cotterell View a PDF of the paper titled Learning to Reason Efficiently with A* Post-Training, by Andreas Opedal and 5 other authors View PDF HTML (experimental) Abstract:Many applications of large language models (LLMs) require deductive reasoning, yet models frequently produce incorrect or redundant inference steps. We frame natural language inference as a search problem where the final answer is the valid proof itself, requiring a reasoning procedure in which intermediate inferences are correct.
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