… The following papers were recommended by the Semantic Scholar API PRAISE: Prefix-Based Rollout Reuse in Agentic Search Training 2026 Train at Moving Edge: Online-Verified Prompt Selection for Efficient RL Training of Large Reasoning Model 2026 Accelerating RL Post-Training Rollouts via System-Integ… …
… AI-generated summary RL post-training of frontier language models is increasingly bottlenecked by autoregressive rollout generation , making rollout acceleration a central systems challenge. …
… At the Inter-Reliability level, Stream-R1 rescales each rollout 's loss by an exponential of a pretrained video reward score , so that rollout s with reliable supervision dominate optimization. …
… AI-generated summary Asynchronous reinforcement learning improves rollout throughput for large language model agents by decoupling sample generation from policy optimization, but it also introduces a critical failure mode for PPO-style off-policy correction . …
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Papers arxiv:2605.09262 Reinforcing Multimodal Reasoning Against Visual Degradation Published on May 10 Submitted by Rui Liu on May 12 Tencent Hunyuan Authors: , , , , , Runpeng Dai , , , Abstract ROMA is an RL fine-tuning framework that enhances multimodal large language models' robustness against… …
… We then train a family of capable Claw-style models, termed ClawGym-Agents , through supervised fine-tuning on black-box rollout trajectories , and further explore reinforcement learning via a lightweight pipeline that parallelizes rollouts across per-task sandboxes .To support reliable evaluation,… …
… GE-Sim 2.0 tops the public WorldArena leaderboard at only 2B parameters, outperforming both dedicated robotic world models and closed-source general video generators, and policies trained against its rollouts and rewards translate into measurable real-world gains, establishing GE-Sim 2.0 as a pract… …
… We jointly train both levels with a hierarchical GRPO-style rollout, enhanced by diverse strategy sampling and critical self-judgment. Does it work? StraTA improves both sample efficiency and final performance, outperforming both frontier closed-source models and prior RL baselines. …