# Seer: Online Context Learning for Fast Synchronous LLM Reinforcement Learning

arXiv: https://arxiv.org/abs/2511.14617 · HTML: https://mlsanity.com/paper/2511.14617/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 60 references that are in the map.
- First posted on arXiv 18 Nov 2025.

## Builds on (most cited first, 30 of 45)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [DAPO: An Open-Source LLM Reinforcement Learning System at Scale](https://mlsanity.com/paper/2503.14476/) (2025)
- [HybridFlow: A Flexible and Efficient RLHF Framework](https://mlsanity.com/paper/2409.19256/) (2024)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [Fast Inference from Transformers via Speculative Decoding](https://mlsanity.com/paper/2211.17192/) (2022)
- [GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints](https://mlsanity.com/paper/2305.13245/) (2023)
- [Kimi k1.5: Scaling Reinforcement Learning with LLMs](https://mlsanity.com/paper/2501.12599/) (2025)
- [SGLang: Efficient Execution of Structured Language Model Programs](https://mlsanity.com/paper/2312.07104/) (2023)
- [DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model](https://mlsanity.com/paper/2405.04434/) (2024)
- [Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads](https://mlsanity.com/paper/2401.10774/) (2024)
- [Group Sequence Policy Optimization](https://mlsanity.com/paper/2507.18071/) (2025)
- [EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty](https://mlsanity.com/paper/2401.15077/) (2024)
- [Muon is Scalable for LLM Training](https://mlsanity.com/paper/2502.16982/) (2025)
- [EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees](https://mlsanity.com/paper/2406.16858/) (2024)
- [EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test](https://mlsanity.com/paper/2503.01840/) (2025)
- [Break the Sequential Dependency of LLM Inference Using Lookahead Decoding](https://mlsanity.com/paper/2402.02057/) (2024)
- [AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning](https://mlsanity.com/paper/2505.24298/) (2025)
- [Llumnix: Dynamic Scheduling for Large Language Model Serving](https://mlsanity.com/paper/2406.03243/) (2024)
- [DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales](https://mlsanity.com/paper/2308.01320/) (2023)
- [Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library](https://mlsanity.com/paper/2506.06122/) (2025)
- [Efficient LLM Scheduling by Learning to Rank](https://mlsanity.com/paper/2408.15792/) (2024)
- [OPT-Tree: Speculative Decoding with Adaptive Draft Tree Structure](https://mlsanity.com/paper/2406.17276/) (2024)
- [StreamRL: Scalable, Heterogeneous, and Elastic RL for LLMs with Disaggregated Stream Generation](https://mlsanity.com/paper/2504.15930/) (2025)
- [Optimizing RLHF Training for Large Language Models with Stage Fusion](https://mlsanity.com/paper/2409.13221/) (2024)
- [NeMo-Aligner: Scalable Toolkit for Efficient Model Alignment](https://mlsanity.com/paper/2405.01481/) (2024)

## Built on by (most cited first, 25 of 25)

- [FP8-RL: A Practical and Stable Low-Precision Stack for LLM Reinforcement Learning](https://mlsanity.com/paper/2601.18150/) (2026)
- [Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learning](https://mlsanity.com/paper/2605.02913/) (2026)
- [Breaking Entropy Bounds: Accelerating RL Training via MTP with Rejection Sampling](https://mlsanity.com/paper/2606.12370/) (2026)
- [Heddle: A Distributed Orchestration System for Agentic RL Rollout](https://mlsanity.com/paper/2603.28101/) (2026)
- [ERPO: Token-Level Entropy-Regulated Policy Optimization for Large Reasoning Models](https://mlsanity.com/paper/2603.28204/) (2026)
- [Draft Less, Retrieve More: Hybrid Tree Construction for Speculative Decoding](https://mlsanity.com/paper/2605.20104/) (2026)
- [Jackpot: Optimal Budgeted Rejection Sampling for Extreme Actor-Policy Mismatch Reinforcement Learning](https://mlsanity.com/paper/2602.06107/) (2026)
- [Fast LLM Post-training via Decoupled and Fastest-of-N Speculation](https://mlsanity.com/paper/2511.16193/) (2025)
- [Libra: Efficient Resource Management for Agentic RL Post-Training](https://mlsanity.com/paper/2606.03077/) (2026)
- [TideRL: Boosting Agentic RL Goodput with Readiness-Aware Scheduling](https://mlsanity.com/paper/2608.10402/) (2026)
- [ReLibra: Routing-Replay-Guided Load Balancing for MoE Training in Reinforcement Learning](https://mlsanity.com/paper/2605.08639/) (2026)
- [Faster Synchronous On-Policy RL via Straggler-Aware Group Sizing](https://mlsanity.com/paper/2606.02218/) (2026)
- [Harnessing Routing Foresight for Micro-step-level MoE load balancing in RL Post-training](https://mlsanity.com/paper/2606.11867/) (2026)
- [TailSieve: Partial-Rollout-Guided Tail Routing for LLM Rollouts](https://mlsanity.com/paper/2608.22788/) (2026)
- [AREAL-DTA: Dynamic Tree Attention for Efficient Reinforcement Learning of Large Language Models](https://mlsanity.com/paper/2602.00482/) (2026)
- [JigsawRL: Assembling RL Pipelines for Efficient LLM Post-Training](https://mlsanity.com/paper/2604.23838/) (2026)
- [Off-Policy Value-Based Reinforcement Learning for Large Language Models](https://mlsanity.com/paper/2603.23355/) (2026)
- [Spotlight: Synergizing Seed Exploration and Spot GPUs for DiT RL Post-Training](https://mlsanity.com/paper/2606.19004/) (2026)
- [FAST: A Framework for Aligned Sampling and Training in Parallel Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/2606.21587/) (2026)
- [TensorCast: The Missing Tensor Management Layer in Large Language Model Infrastructure](https://mlsanity.com/paper/2608.06007/) (2026)
- [Belayer: Efficient Fault Tolerance for LLM Agentic RL Training](https://mlsanity.com/paper/2608.14635/) (2026)
- [SPLASH: Switching Parallel Layouts of Attention with Seamless Handoff for LLM Serving](https://mlsanity.com/paper/2609.37626/) (2026)
- [ThunderSyncRL: Lossless Acceleration of Agentic Reinforcement Learning](https://mlsanity.com/paper/2610.05935/) (2026)
- [S3R: Fast and Faithful RL Rollouts via Self-Sparse Speculative Decoding](https://mlsanity.com/paper/2610.13202/) (2026)
- [Prorsa: Unleashing the Potential of Prefill-Decode Disaggregation in Agentic Reinforcement Learning](https://mlsanity.com/paper/2610.16592/) (2026)

## Abstract (arXiv)

Reinforcement Learning (RL) has emerged as a critical technique 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 context learning RL system that addresses these challenges through a key observation: requests sharing the same prompt exhibit strong similarities in output lengths and response patterns. Leveraging this insight, Seer introduces three coordinated techniques: (1) divided rollout for dynamic load balancing, (2) context-aware scheduling to mitigate long-tail request delays, and (3) adaptive grouped speculative decoding to accelerate generation. These mechanisms work in concert to markedly reduce long-tail latency and improve resource efficiency during rollout. Evaluations on production-grade RL workloads demonstrate that Seer achieves up to 2.04× end-to-end rollout throughput improvement compared to the state-of-the-art synchronous RL systems, while notably reducing long-tail latency by 72-94%.