Restoring Exploration after Post-Training: Latent Exploration Decoding for Large Reasoning Models

• URL: http://arxiv.org/abs/2602.01698v1
• Date: Mon, 02 Feb 2026 06:12:33 GMT
• Title: Restoring Exploration after Post-Training: Latent Exploration Decoding for Large Reasoning Models
• Abstract summary: Latent Exploration Decoding (LED) is a depth-conditioned decoding strategy.<n>LED consistently improves pass@1 and pass@16 accuracy by 0.61 and 1.03 percentage points across multiple reasoning benchmarks and models.
• Score: 49.453104834621286
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Reasoning Models (LRMs) have recently achieved strong mathematical and code reasoning performance through Reinforcement Learning (RL) post-training. However, we show that modern reasoning post-training induces an unintended exploration collapse: temperature-based sampling no longer increases pass@$n$ accuracy. Empirically, the final-layer posterior of post-trained LRMs exhibit sharply reduced entropy, while the entropy of intermediate layers remains relatively high. Motivated by this entropy asymmetry, we propose Latent Exploration Decoding (LED), a depth-conditioned decoding strategy. LED aggregates intermediate posteriors via cumulative sum and selects depth configurations with maximal entropy as exploration candidates. Without additional training or parameters, LED consistently improves pass@1 and pass@16 accuracy by 0.61 and 1.03 percentage points across multiple reasoning benchmarks and models. Project page: https://GitHub.com/Xiaomi-Research/LED.

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