arXiv:2606. 05464v1 Announce Type: new Abstract: Verifiable reward training has improved mathematical and coding reasoning, but these domains capture only part of step-by-step decision making.
By Nicol\'as Astorga, Nabeel Seedat, Mihaela van der Schaar
arXiv:2602.13551v3 Announce Type: replace
Abstract: Reward models (RMs) play a central role throughout the language model (LM) pipeline, particularly in non-verifiable domains. However, the dominant...
By Yike Wang, Faeze Brahman, Shangbin Feng, Teng Xiao, Hannaneh Hajishirzi, Yulia Tsvetkov
The paper introduces probe-driven test-time reinforcement learning (TTRL) for code generation, using probe inputs derived from problem statements to evaluate candidate programs and define a Probe Consensus Reward (PCR). To address PCR’s unreliability and prevent reward hacking, the authors propose Entropy-Regularized Rank-Masked Policy Optimization (ERPO), which applies rank masking and an entropy ceiling to produce conservative policy updates. Experiments on coding benchmarks show that ERPO significantly improves pass@1 and pass@k metrics in both in-domain adaptation and zero-shot transfer scenarios.
By Jiacheng Xu, Feng Chen, Xiuneng Xu, Bo An
The paper introduces APIVIS, a training-time framework that integrates finite-budget Gumbel search into reinforcement learning with verifiable rewards (RLVR) for mathematical reasoning. APIVIS combines direct and searched responses within each rollout group, uses selective supervision on search-improved tokens, and applies value-guided selection to improve verifier rewards at each searched state. Experiments on standard mathematical reasoning benchmarks and various model scales show significant performance gains over existing search-based methods.
By Shaohuai Liu, Yuning Wu, Haoran Liu, Enzo Jia, Devin Chen, Kai Wei
arXiv:2608. 09217v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a central post-training paradigm for eliciting reasoning capabilities in large language models, yet uniform task sampling allocates compute without regard to differences in how tasks respond to optimization.
By Ting Zhou, Zhenqing Ling, Daoyuan Chen, Qianli Shen, Yilun Huang, Ying Shen, Yaliang Li
arXiv:2609.38672v1 Announce Type: cross
Abstract: Beam-search-based test-time methods provide an effective way to improve large language model (LLM) performance on long-horizon generation by pruning...
By Qijia He, Yu Huang, Yuan Cheng, Yuxin Chen, Yingbin Liang