Recently, Moonlight AI and Tsinghua University collaboratively released a paper that delves into a reinforcement learning training system named Seer. This system significantly accelerates the training speed of large models in reinforcement learning, all without modifying the core training algorithms.

When evaluated on real-world, production-grade RL workloads, Seer demonstrated a remarkable increase in end-to-end deployment throughput, ranging from 74% to 97%. Additionally, it reduced long-tail latency by 75% to 93%, thereby substantially speeding up the iteration process in RL training.

The paper highlights that current synchronous RL systems encounter significant performance bottlenecks. These include notable long-tail latency and inefficient resource utilization during the deployment phase.

Seer tackles these issues by capitalizing on the similarities in output length and generation patterns among requests that share the same prompt. It introduces three pivotal technologies: segmented deployment for dynamic load balancing, context-aware scheduling, and adaptive grouped speculative decoding.

These mechanisms collectively lead to a substantial reduction in long-tail latency during deployment and enhance overall resource efficiency.

Traditional group-level deployment approaches treat request groups as single entities, which often results in severe load imbalances both across and within instances. Seer, however, achieves dynamic load balancing and prevents preemption through its segmented deployment strategy. Building on this, Seer enables online in-context learning, which supports context-aware scheduling and adaptive grouped speculative decoding, further shortening deployment times.

Seer utilizes Divided Rollout to attain dynamic load balancing and maximize GPU memory usage. It not only divides request groups into individual requests but also breaks them down into multiple blocks for incremental scheduling and distribution. This tactic ensures optimal resource utilization without incurring expensive preemption costs throughout the rollout.

Seer employs a speculative request mechanism to steer its scheduling strategy. By generating a high-priority response from each GRPO group, it can estimate the group's expected generation length and KVCache footprint in real-time. This allows the global scheduler to implement an approximate longest-job-first strategy.

This method prioritizes longer tasks to run concurrently with shorter ones, maximizing batch processing density.

Seer introduces a speculative decoding mechanism based on online in-context learning to expedite deployment. It deploys a Distributed Grouped Draft Server (DGDS), which creates a highly accurate dynamic draft model that is inherently synchronized with the target model.

Furthermore, DGDS incorporates an adaptive draft scope mechanism to maximize system throughput. Adaptive grouped speculative decoding outperforms two ablation variants by 27% and 11%, respectively, in terms of end-to-end throughput.

To assess Seer's end-to-end performance, researchers compared its throughput with that of the synchronous reinforcement learning system veRL. The findings revealed that, despite significant variations in model sizes and workload characteristics across different RL tasks, Seer achieved substantial acceleration in all tasks, with throughput improvements ranging from 74% to 97% compared to veRL.

This enhancement is attributed to Seer's fine-grained load balancing and its capability to learn and group request context information online, enabling superior scheduling strategies and more efficient grouped speculative decoding.

The benchmark system displayed considerable fluctuations in completion time and throughput during iterations. This instability stemmed from its group-level scheduling approach, where resource utilization was heavily impacted by the randomness of initial request allocation. In contrast, Seer's fine-grained scheduling and dynamic load balancing significantly reduced volatility in resource utilization.

For a more in-depth analysis, researchers conducted a statistical examination of long-tail latency phenomena during the deployment process.

The results uncovered that tail latency poses a significant challenge during deployment, especially for memory-constrained tasks like Moonlight and Qwen2-VL-72B. In these cases, the last 10% of requests accounted for up to 50% of the total execution time.

Due to the absence of length information, request queuing and preemption mechanisms led to scheduling delays for long-output requests, allowing a few requests to dominate the final execution phase. Secondly, single request groups resulted in load imbalances across instances, with some instances receiving requests of extremely long average lengths, leading to tail latency. Seer significantly reduced tail latency by 75% to 93% through online in-context learning and fine-grained request scheduling, thereby substantially boosting system throughput.

References:

https://arxiv.org/pdf/2511.14617