ForeMoE uses routing foresight from the rollout stage to enable micro-step load balancing in MoE RL post-training via a hierarchical planner and transfer engine, claiming up to 1.45x speedup on 64 GPUs.
Part ii: Roll flash–accelerating rlvr and agentic training with asynchrony
7 Pith papers cite this work. Polarity classification is still indexing.
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Freshness-Aware PER augments prioritized experience replay with exponential age decay based on effective sample size to enable successful reuse of trajectories in LLM and VLM reinforcement learning, outperforming on-policy baselines on agentic tasks.
NExt accelerates RLVR training for LLMs by nonlinearly extrapolating low-rank parameter trajectories extracted from LoRA runs.
AsyncWebRL reports up to 2.9x training speedup and new SOTA on WebGym OOD split via async overlap plus constant normalizer in GRPO, with largest gains on harder tasks.
ROSE is a system for cooperative elasticity that co-locates serving and rollout models on shared GPUs, delivering 1.3-3.3x higher end-to-end throughput than fixed-resource baselines while preserving serving SLOs.
Seer improves synchronous LLM RL rollout throughput by up to 2.04x and reduces long-tail latency by 72-94% via divided rollout, context-aware scheduling, and adaptive grouped speculative decoding based on prompt similarity observations.
DARTS accelerates LLM RL training up to 1.77x by distribution-aware trajectory sampling and adaptive redundancy allocation that shapes rollouts toward conciseness without performance loss.
citing papers explorer
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Harnessing Routing Foresight for Micro-step-level MoE load balancing in RL Post-training
ForeMoE uses routing foresight from the rollout stage to enable micro-step load balancing in MoE RL post-training via a hierarchical planner and transfer engine, claiming up to 1.45x speedup on 64 GPUs.
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Freshness-Aware Prioritized Experience Replay for LLM/VLM Reinforcement Learning
Freshness-Aware PER augments prioritized experience replay with exponential age decay based on effective sample size to enable successful reuse of trajectories in LLM and VLM reinforcement learning, outperforming on-policy baselines on agentic tasks.
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Low-rank Optimization Trajectories Modeling for LLM RLVR Acceleration
NExt accelerates RLVR training for LLMs by nonlinearly extrapolating low-rank parameter trajectories extracted from LoRA runs.
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AsyncWebRL: Efficient Multi-Step RL for Visual Web Agents
AsyncWebRL reports up to 2.9x training speedup and new SOTA on WebGym OOD split via async overlap plus constant normalizer in GRPO, with largest gains on harder tasks.
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ROSE: Rollout On Serving GPUs via Cooperative Elasticity for Agentic RL
ROSE is a system for cooperative elasticity that co-locates serving and rollout models on shared GPUs, delivering 1.3-3.3x higher end-to-end throughput than fixed-resource baselines while preserving serving SLOs.
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DARTS: Distribution-Aware Active Rollout Trajectory Shaping for Accelerating LLM Reinforcement Learning
DARTS accelerates LLM RL training up to 1.77x by distribution-aware trajectory sampling and adaptive redundancy allocation that shapes rollouts toward conciseness without performance loss.