Reinforcement learning over a frozen reasoner has become a common recipe for teaching a policy which external tools to invoke. We show that this recipe becomes structurally mismatched in specialist sc...
arXiv:2610.09804v1 Announce Type: new
Abstract: Reinforcement learning is now central to eliciting reasoning in large language models, while in the popular algorithm Group Relative Policy Optimizatio...
By Yingxiang Yang, Weihang Xiao, Zhunxuan Wang, Joshua Flashner, Niresh Agarwal
arXiv:2607. 00531v1 Announce Type: cross Abstract: Scientific reasoning is an increasingly important capability of large language models, yet improving the robustness and efficiency of training such reasoning remains a key open challenge.
By Xuefeng Liu, Mingxuan Cao, Qinan Huang, Thomas Brettin, Rick Stevens, Le Cong
ReAPO: Retry-Aware Policy Optimization for Efficient Exploration in LLM Reasoning proposes a new approach to improve language‑model reasoning by addressing the inefficiencies of independent stochastic rollouts. By framing credit assignment as a Stochastic Shortest Path problem, ReAPO modifies GRPO to allocate more generation to failed trajectories, reinforcing successful retries without rewarding failures. Experiments on Qwen3 and OLMo‑2 models across eight mathematical and scientific benchmarks show that ReAPO outperforms GRPO and other baselines, achieving higher first‑attempt accuracy and reducing generated tokens needed to reach peak performance.
By Nghi Dao, Calder Morton-Ferguson, Noah Golowich, Adam Block
The paper investigates Evolution Strategies (ES) as a memory‑efficient post‑training method for large language model (LLM) reasoning. It demonstrates that ES outperforms Group Relative Policy Optimization (GRPO) by achieving broader reasoning coverage, improving Pass@K metrics, and avoiding entropy collapse. The study also reveals that ES’s performance gains stem from sparse, high‑magnitude parameter updates, do not cause catastrophic forgetting, and can be combined with GRPO in a sequential training strategy.
By Yunpeng Ba, Zhi Zheng, Yue Xie, Jiaqing Li, Xialiang Tong, Tao Zhong, Mingxuan Yuan, Zhichao Lu, Xuyang Wu, Zhenkun Wang
arXiv:2607. 26253v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) is bottlenecked by rollout generation, yet many sampled prompts produce saturated groups (all responses correct or all incorrect) whose zero reward variance yields no policy-gradient signal.
By Pixel Nomand, Elena Voss, Marcus Hale, Sofia Reyes