Seer: Online Context Learning for Fast Synchronous LLM Reinforcement Learning
This work addresses efficiency problems for developers and researchers using RL to train LLMs, representing an incremental optimization of existing synchronous RL systems.
The paper tackles performance bottlenecks in synchronous reinforcement learning (RL) systems for large language models (LLMs) by introducing Seer, an online context learning system that reduces long-tail latency and improves resource utilization during rollout. It achieves a 74% to 97% improvement in end-to-end rollout throughput and a 75% to 93% reduction in long-tail latency compared to state-of-the-art systems.
Reinforcement Learning (RL) has become critical for advancing modern Large Language Models (LLMs), yet existing synchronous RL systems face severe performance bottlenecks. The rollout phase, which dominates end-to-end iteration time, suffers from substantial long-tail latency and poor resource utilization due to inherent workload imbalance. We present Seer, a novel online context learning system that addresses these challenges by exploiting previously overlooked similarities in output lengths and generation patterns among requests sharing the same prompt. Seer introduces three key techniques: divided rollout for dynamic load balancing, context-aware scheduling, and adaptive grouped speculative decoding. Together, these mechanisms substantially reduce long-tail latency and improve resource efficiency during rollout. Evaluations on production-grade RL workloads demonstrate that Seer improves end-to-end rollout throughput by 74% to 97% and reduces long-tail latency by 75% to 93% compared to state-of-the-art synchronous RL systems, significantly accelerating RL training iterations.