From the Inside Out: Progressive Distribution Refinement for Confidence Calibration
Leveraging the model's internal information as the self-reward signal in Reinforcement Learning (RL) has received extensive attention due to its label-free nature. While prior works have made significant progress in applying the Test-Time Scaling (TTS) strategies to RL, the discrepancy in internal information between test and training remains inadequately addressed. Moreover, Test-Time Training based on voting-based TTS strategies often suffers from reward hacking problems. To address these issues, we propose DistriTTRL, which leverages the distribution prior of the model's confidence during RL to progressively optimize the reward signal, rather than relying solely on single-query rollout...
| Authors | Xizhong Yang, Yinan Xia, Huiming Wang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 17 March 2026 |
| Last updated at source | 17 March 2026 |
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