arXiv Machine Learning
QwenGyre: An Elastic Reinforcement Learning Framework for Training xLong-Horizon Agents
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arXiv AI
Jun 3
Libra: Efficient Resource Management for Agentic RL Post-Training
arXiv:2606. 03077v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a standard post-training paradigm for large language models (LLMs), extending beyond preference alignment to complex reasoning and multi-turn agentic behaviors.
arXiv Machine Learning
Aug 12
TideRL: Boosting Agentic RL Goodput with Readiness-Aware Scheduling
arXiv:2608. 10402v1 Announce Type: new Abstract: Reinforcement learning (RL) for large language models is moving toward multi-turn agentic workloads, where rollout tasks repeatedly pause for external environments, resume with growing contexts, and finish at highly variable times.
arXiv AI
Jun 16
RollArt: Disaggregated Multi-Task Agentic RL Training at Scale
arXiv:2512. 22560v2 Announce Type: replace-cross Abstract: Agentic Reinforcement Learning (RL) trains LLMs through multi-turn interactions with environments, producing workloads that mix compute-bound prefill, bandwidth-bound decoding, CPU-heavy environment execution, and bursty reward evaluation.
arXiv AI
Jul 9
Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning
arXiv:2607. 07508v1 Announce Type: cross Abstract: Reinforcement learning (RL) is becoming increasingly important for post-training large language models (LLMs).
arXiv Machine Learning
Aug 4
Reusing Rollouts under Policy Lag: Prefix-Normalized Policy Optimization for LLM Reinforcement Learning
arXiv:2608. 01418v1 Announce Type: cross Abstract: Autoregressive rollout generation is a major computational cost in reinforcement learning for large language models.
arXiv Machine Learning
Oct 8
RollVerify: Bridging Efficiency and Accuracy in Long-Tail Rollout Reinforcement Learning
RollVerify is a lightweight reinforcement learning framework that addresses the trade‑off between efficiency and accuracy in long‑tail rollout settings. It introduces an off‑policy shift metric (OPS) to quantify deviation in partially generated trajectories and uses sequence‑level and token‑level verification to truncate invalid suffixes before training. Experiments on mathematical reasoning tasks show that RollVerify matches on‑policy accuracy while cutting training costs, with preliminary evidence from code‑generation tasks.