Seer: Online Context Learning for Fast Synchronous LLM Reinforcement Learning
Talkat 20th USENIX Symposium on Operating Systems Design and Implementation (OSDI '26) · 2026-07-13..07-15·
summary
Seer claims synchronous on‑policy RL rollouts speed up by up to 2.04× and cuts long‑tail latency 72–94% by exploiting intra‑group similarities in length and token patterns. It implements divided rollout (chunked generation + global Mooncake RDMA/SSD cache), a request buffer and context manager for context‑aware scheduling, and a distributed draft server with compressed suffix trees for adaptive grouped speculative decoding. Evaluations on production RL workloads report 44–104% overall throughput gains while preserving training quality with no algorithmic loss.
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chapters (15)
- 0:00Seer introduction
- 0:19Reasoning models need RIL
- 1:44Time in RL training loops
- 3:09Memory volatility in reasoning
- 3:53Rollout imbalance issues
- 5:17Limitations of async decoding
- 6:21GRPO similar response groups
- 7:47Chunk execution, group learning
- 8:53Seer architecture overview
- 9:56Divided rollout method
- 11:22Context-aware scheduling
- 12:26Adaptive grouped speculative decoding
- 14:58Evaluation results
- 16:26Strict sync training importance
- 17:31Summary
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