Seer: Online Context Learning for Fast Synchronous LLM Reinforcement Learning (Nov 2025)

Title: Seer: Online Context Learning for Fast Synchronous LLM Reinforcement Learning (Nov 2025)
Link: http://arxiv.org/abs/2511.14617v1
Date: November 2025

Summary:
This paper introduces Seer, a system optimized for the rollout phase of synchronous Reinforcement Learning (RL) in Large Language Models (LLMs), specifically addressing the bottlenecks caused by long Chain-of-Thought reasoning. Seer overcomes challenges like long-tail latency and memory inefficiency by treating prompt groups not as monolithic units but as flexible, contextual resources. The system implements three core techniques: 'Divided Rollout' for sub-request level load balancing, 'Context-Aware Scheduling' which uses speculative probes to predict output lengths for better scheduling, and 'Adaptive Grouped Speculative Decoding' to leverage pattern similarities within groups for faster inference. Experiments demonstrate that Seer improves throughput by 74-97% and reduces long-tail latency by 75-93% compared to state-of-the-art baselines.

Key Topics:
Reinforcement Learning (RL)
Large Language Models (LLMs)
Synchronous Rollout
Speculative Decoding
Context-Aware Scheduling
Long-tail Latency
Divided Rollout

Chapters:
00:00 - RL Rollout Bottlenecks
01:02 - Sierra System Overview
02:09 - Analyzing Efficiency Challenges
04:55 - Leveraging GRPO Structure
06:12 - Implementing Divided Rollout
08:02 - Context-Aware Scheduling
10:04 - Model-Free Speculative Decoding
11:45 - Adaptive Decoding Performance
12:53 - Results and Future Implications

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