Seer: Online Context Learning for Fast Synchronous RL Rollouts

Seer: Online Context Learning for Fast Synchronous RL Rollouts

Moonshot AI and Tsinghua University introduce Seer, a system that accelerates synchronous reinforcement learning (RL) rollouts by 74-97% through online context learning. This addresses the bottleneck of long tail requests and KV cache fragmentation in large language model training.

Why This Matters

Synchronous RL rollouts for large models are bottlenecked by long tail requests and inefficient KV cache usage, which waste GPU resources and increase training time. Traditional systems spend up to 50% of iteration time on the last 10% of requests, but Seer reduces this tail latency by 75-93% while maintaining on-policy behavior.

Key Insights

• “74-97% rollout throughput gain over veRL baseline, 2025” (Moonshot AI paper)
• “Divided rollout + context-aware scheduling reduces tail latency by 75-93%” (Seer architecture)
• “Mooncake-based Global KVCache Pool enables request migration without recomputing prefills” (Moonshot AI, Tsinghua)

Practical Applications

• Use Case: Large language models with long chain-of-thought outputs (e.g., Moonlight, Qwen2 VL 72B)
• Pitfall: Ignoring context-aware scheduling leads to high tail latency and GPU underutilization

References:

• https://www.marktechpost.com/2025/11/22/moonshot-ai-researchers-introduce-seer-an-online-context-learning-system-for-fast-synchronous-reinforcement-learning-rl-rollouts/