Distribution-Aware Reward Estimation for Test-Time Reinforcement Learning

• URL: http://arxiv.org/abs/2601.21804v1
• Date: Thu, 29 Jan 2026 14:48:02 GMT
• Title: Distribution-Aware Reward Estimation for Test-Time Reinforcement Learning
• Abstract summary: Test-time reinforcement learning (TTRL) enables large language models to self-improve on unlabeled inputs.<n>Most existing TTRL methods rely on majority voting (MV) over rollouts to produce deterministic rewards.<n>We propose Distribution-AwareReward Estimation (DARE) which shifts reward estimation from a single majority outcome to the full empirical rollout distribution.
• Score: 10.571602226707473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Test-time reinforcement learning (TTRL) enables large language models (LLMs) to self-improve on unlabeled inputs, but its effectiveness critically depends on how reward signals are estimated without ground-truth supervision. Most existing TTRL methods rely on majority voting (MV) over rollouts to produce deterministic rewards, implicitly assuming that the majority rollout provides a reliable learning signal. We show that this assumption is fragile: MV reduces the rollout distribution into a single outcome, discarding information about non-majority but correct actions candidates, and yields systematically biased reward estimates. To address this, we propose Distribution-AwareReward Estimation (DARE), which shifts reward estimation from a single majority outcome to the full empirical rollout distribution. DARE further augments this distribution-based reward with an exploration bonus and a distribution pruning mechanism for non-majority rollout exploration and reward denoise, yielding a more informative and robust reward estimation. Extensive experiments on challenging reasoning benchmarks show that DARE improves optimization stability and final performance over recent baselines, achieving relative improvements of 25.3% on challenging AIME 2024 and 5.3% on AMC.

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