arXiv:2606. 07950v1 Announce Type: new Abstract: RL with verifiable rewards can substantially improve LLM reasoning, yet standard GRPO-style training often treats easy, hard, and learnable questions alike through uniform sampling and weighting, leading to inefficient compute allocation.
By Zhanke Zhou, Xiangyu Lu, Chentao Cao, Brando Miranda, Tongliang Liu, Bo Han, Sanmi Koyejo
arXiv:2606. 05606v1 Announce Type: new Abstract: LLM post-training often relies on reinforcement learning methods that sample multiple rollouts per prompt, yet most existing approaches use a fixed rollout budget for every prompt, despite large differences in the training signal different prompts provide.
By Yiming Zong, Yige Wang, Jiashuo Jiang
arXiv:2610.34951v1 Announce Type: cross
Abstract: Recent methods for Reinforcement Learning with Verifiable Rewards (RLVR) use rollout diversity to improve reasoning performance or training efficienc...
By Sohyun Son, Yerim Hong, Jaeyun Park, Mujeen Sung
arXiv:2605.27293v2 Announce Type: replace
Abstract: Reinforcement learning with verifiable rewards has become a standard recipe for improving the reasoning abilities of large language models. Existin...
By Shijin Gong, Erhan Xu, Kai Ye, Giulia Livieri, Francesco Quinzan, Chengchun Shi
The paper introduces CLOSEDLOOPGAUSSIAN, an adaptive curriculum strategy for Group Relative Policy Optimization (GRPO) that dynamically adjusts the pace and spread of an Easy-to-Hard Gaussian sampler based on probe estimates of per-difficulty accuracy. Unlike fixed easy-to-hard schedules, the method accelerates training when the model outpaces a reference curriculum, slows it down when it lags, and broadens sampling when earlier difficulties regress, thereby keeping training focused on learnable yet challenging tasks. Experiments on symbolic arithmetic reasoning demonstrate that CLOSEDLOOPGAUSSIAN outperforms vanilla GRPO and fixed open-loop curricula, especially in intermediate-difficulty regimes where group-relative learning signals are most informative.
By Yuv Raj Pant, Moshiur Rahman Tonmoy, Kaiqun Fu
The paper introduces Single‑Rollout Pass@k Policy Optimization (SR‑PPO), a reinforcement learning method that uses only one rollout per prompt to train language‑model agents. SR‑PPO combines Monte Carlo token‑level advantage estimation with Pass@k gradient reweighting and an online critic for baseline and weight estimation. Experiments show that SR‑PPO achieves stable learning and performance comparable to traditional Group Relative Policy Optimization (GRPO) on reasoning and logic benchmarks, even when off‑policy drift degrades standard Pass@1 PPO.
By Fengdi Che, Yang Liu, Lei Yu, Meng Cao, Tong Che, Rupam Mahmood, Dale Schuurmans