Seer: Accelerating Large Language Model Reinforcement Learning with Online Context Learning

Reinforcement learning has become a cornerstone in developing state-of-the-art large language models, enabling significant breakthroughs in complex reasoning and problem-solving capabilities. However, traditional synchronous reinforcement learning systems face severe performance bottlenecks during the rollout phase—particularly long-tail latency and poor resource utilization. Have you ever experienced training processes slowing down because a handful of long-text generation requests dragged down overall progress? This represents a typical challenge when existing systems handle long-chain reasoning tasks.

Addressing this challenge, the Seer system emerges as a groundbreaking solution. Through online context learning technology, it fully leverages similarities between multiple requests under the same prompt, achieving efficient scheduling and inference acceleration during the rollout phase. Experimental results demonstrate that Seer improves end-to-end rollout throughput by 74% to 97% while reducing long-tail latency by 75% to 93%. This article provides an in-depth analysis of Seer’s working principles, core technologies, and practical effectiveness, offering comprehensive insights into this revolutionary improvement for synchronous reinforcement learning training.

Why the Rollout Phase is Critical in Reinforcement Learning

To understand Seer’s value, we must first grasp the fundamental workflow of reinforcement learning training. In modern large language model training, reinforcement learning iterations typically consist of these phases:

1. Rollout Phase: The model generates response trajectories based on given prompt sets
2. Reward Computation: A dedicated reward server evaluates the quality of generated responses
3. Experience Collection: Algorithms compute training experiences required for policy updates
4. Training Phase: Model parameters are updated using collected experiences
5. Weight Update: Updated weights are propagated to the inference model for the next iteration

Among these five phases, the rollout phase dominates. Data from production environments shows that the rollout phase consumes 63% to 87% of the entire iteration time. This means that optimizing rollout phase efficiency will most directly impact the entire reinforcement learning training process.

Specific Challenges in the Rollout Phase

The core problems in the rollout phase stem from two characteristics of long-chain reasoning tasks:

Challenge One: Massive Fluctuations in Memory Consumption

When models generate responses averaging tens of thousands of tokens, the KVCache storing intermediate results may occupy several gigabytes of memory. In algorithms like GRPO, where each prompt requires generating 8-16 responses, memory consumption multiplies. This volatility forces systems to choose between two unfavorable scenarios:

• Reserving sufficient memory space, resulting in extremely low early-stage resource utilization
• Pursuing high concurrency, only to face request interruptions due to insufficient memory later, triggering expensive recomputation

Challenge Two: Severe Long-tail Effect

The long-tail problem is particularly prominent during the rollout phase. Data indicates that the last 10% of completed requests may consume up to 50% of the total execution time. This primarily stems from two factors:

1. Request groups under the same prompt often share similar length characteristics, causing “entire groups” of long requests to be assigned to the same instance, creating cross-instance load imbalance
2. Under memory constraints, long requests may be delayed in scheduling, further exacerbating tail latency

Illustration: The distribution of output lengths during rollout is extremely uneven, ranging from hundreds to 98K tokens, featuring both high average length and significant variance.

Seer’s Breakthrough Design: Online Context Learning

The core insight of the Seer system lies in discovering a crucial phenomenon overlooked by existing systems: in popular reinforcement learning algorithms like GRPO, multiple responses (typically 8-16) generated under the same prompt show high similarity in output length and generation patterns. This similarity provides valuable contextual information for optimization.

Three Core Technologies of Seer

Seer addresses fundamental problems in the rollout phase through three innovative technologies:

1. Divided Rollout: Achieving Dynamic Load Balancing

Traditional systems schedule entire request groups as indivisible units, causing severe cross-instance and intra-instance load imbalance. Seer breaks this paradigm by introducing a divided rollout mechanism.

How Divided Rollout Works:

• Decomposes each request group not only into independent requests but further divides them into multiple chunks
• Each chunk contains a relatively small number of tokens (e.g., 8K)
• Requests re-enter the scheduling queue after completing the current chunk, iterating until generating an end token or reaching the maximum generation length

Advantages of This Design:

• Fine-grained Memory Management: Maintains nearly constant KVCache occupancy through chunk-based generation, preventing later memory shortages
• Dynamic Load Balancing: The scheduler can dynamically allocate chunks to the most idle instances based on real-time instance load
• Avoiding Interruption Overhead: Combined with a global KVCache pool, eliminates recomputation during request migration

Illustration: Traditional methods cause load imbalance due to whole-group scheduling (left), while Seer achieves dynamic load balancing through division (right).

2. Context-Aware Scheduling: Reducing Long-tail Latency

Building on divided rollout, Seer further utilizes length correlation within request groups to implement intelligent scheduling.

Core Elements of Scheduling Strategy:

• Speculative Request Mechanism: Designates the first request of each group as a high-priority speculative request, serving as a “probe” for the group’s workload
• Length Filtering: Applies shortest-first scheduling to speculative requests, quickly identifying potential long-tail samples
• Approximate Longest-First Scheduling: Prioritizes request groups with longer predicted lengths based on information from speculative requests

Three Stages of the Scheduling Algorithm:

1. Length Filtering: Prioritizes execution of speculative requests using a shortest-first strategy, allowing quick completion of short requests
2. Length Estimation Update: Dynamically updates the group’s predicted length based on actual lengths of completed requests
3. Request Scheduling: Switches to approximate longest-first scheduling strategy after all speculative requests are executed

This scheduling strategy based on real-time length information significantly reduces situations where long requests are delayed, substantially decreasing long-tail latency.

3. Adaptive Grouped Speculative Decoding: Accelerating Inference Process

Speculative decoding accelerates inference by generating draft tokens, but traditional methods face two key limitations in reinforcement learning scenarios:

• Static draft models cannot adapt to continuously evolving target models
• Draft generation produces prohibitive overhead in large-batch scenarios

Seer introduces adaptive grouped speculative decoding to overcome these limitations.

Design of Distributed Grouped Draft Server:

Seer’s DGDS module adopts a master-worker architecture, with the compressed suffix tree data structure at its core, effectively aggregating token sequences from all requests within the same group.

Four Key Steps:

1. Asynchronous Append: Inference instances batch-send newly generated tokens to DGDS
2. Global Aggregation: DGDS aggregates token updates by group, building a unified CST
3. Periodic Fetch: Draft clients in inference instances periodically synchronize the latest CST
4. Local Speculation: Instances perform speculative decoding based on local CST, generating high-quality draft tokens

Advantages of Adaptive Mechanism:

• Dynamically adjusts draft length and path count based on system load
• Reduces draft token count during high concurrency to avoid computational bottlenecks
• Increases draft token count during long-tail stages (low concurrency) to maximize acceleration

Illustration: DGDS enables cross-instance context sharing through distributed architecture, providing high-quality draft tokens for speculative decoding.

Seer’s Performance in Practical Applications

To validate Seer’s actual effectiveness, the research team conducted comprehensive evaluations on real production-level reinforcement learning workloads.

Experimental Setup

Testing employed three models with different scales and architectures:

• Moonlight: 32GB model focused on mathematical reasoning
• Qwen2-VL-72B: 146GB multimodal model handling language-vision mixed reasoning tasks
• Kimi-K2: 1TB large-scale model representing state-of-the-art reasoning capabilities

The baseline system selected was veRL—a highly optimized synchronous training system ensuring fair comparison.

End-to-End Performance Results

Experimental results show that Seer achieved significant performance improvements across all test tasks:

Rollout Throughput Improvement

• Moonlight task: 74% throughput improvement
• Qwen2-VL-72B task: 87% throughput improvement
• Kimi-K2 task: 97% throughput improvement

Compared to the baseline system, Seer not only improved average throughput but also significantly reduced performance fluctuations between iterations. This indicates that Seer’s fine-grained scheduling effectively minimizes the impact of initial request allocation randomness on resource utilization.

Long-tail Latency Reduction

More impressively, Seer demonstrated exceptional performance in reducing long-tail latency:

• In memory-constrained tasks (like Moonlight and Qwen2-VL-72B), long-tail latency reduced by 75% to 93%
• In traditional systems, the last 10% of requests consumed up to 50% of total execution time, while Seer substantially reduced this proportion

Illustration: Seer significantly reduced tail latency across all tasks, thereby decreasing total execution time.

Contribution Analysis of Various Technical Components

To understand each component’s specific contribution, the research team conducted detailed ablation experiments:

Method	Moonlight	Qwen2-VL-72B	Kimi-K2
Baseline	1.00	1.00	1.00
+ Divided Rollout	1.27×	1.31×	1.35×
+ Context-Aware Scheduling	1.33×	1.44×	1.47×
+ Grouped Speculative Decoding	1.77×	1.87×	1.77×

From the table, we can observe:

• Divided Rollout as the foundation brought 27%-35% performance improvement through dynamic load balancing
• Context-Aware Scheduling utilizing length information further improved performance by 6%-13%
• Adaptive Grouped Speculative Decoding contributed the largest single-component improvement at 30%-44%

Illustration: Compared to baseline, Seer maintained higher memory utilization throughout the rollout process while significantly reducing tail stage duration.

Effectiveness of Context-Aware Scheduling

To validate the value of length context, the team compared scheduling effectiveness under different information conditions:

• No Context: Using only divided rollout without length information to guide scheduling
• Actual Seer: Using length prediction information provided by speculative requests
• Oracle: Knowing all outputs’ actual lengths in advance, implementing ideal longest-first scheduling

Experimental results show that Seer’s context-aware scheduling achieved 95% of Oracle performance, proving the effectiveness of its length prediction mechanism. Compared to context-free methods, Seer reduced long-tail latency by 87%.

Advantages of Grouped Speculative Decoding

For speculative decoding, the team explored two key questions:

Does group context help speculative decoding performance?

Experimental data provides a positive answer. Compared to the baseline without speculative decoding:

• Adaptive grouped speculative decoding brought 30% end-to-end throughput improvement
• Without group context sharing, improvement dropped to 19%
• Without adaptive strategy, improvement was only 3%, almost equivalent to not using speculative decoding

How does the adaptive mechanism improve system throughput?

The key lies in the adaptive mechanism’s ability to dynamically adjust strategies according to different stages of the rollout process:

• When short requests dominate and batch sizes are large, the system generates fewer draft tokens, lowering average acceptance length but avoiding computational bottlenecks
• When long requests dominate and batch sizes are small, the system generates more draft tokens, increasing average acceptance length to over 3.5

Illustration: The adaptive mechanism enables the system to generate more draft tokens during the long-tail stage, thereby prioritizing acceleration of long requests.

Practical Implementation Details of Seer

As a complete synchronous reinforcement learning system, Seer’s implementation considers practical requirements of production environments:

System Architecture

Seer adopts a modular design containing three core modules:

1. Inference Engine Pool: Contains multiple inference instances and distributed KVCache pool
2. Request Buffer: Uniformly manages inputs, outputs, and runtime states of all requests
3. Context Manager: Maintains contextual views of all requests, providing context-based scheduling decisions

Global KVCache Pool

Seer constructs a globally shared KVCache pool across inference nodes, employing a DRAM/SSD two-tier storage architecture. To reduce transfer overhead, the KVCache server proactively prefetches KVCache to target instances based on queue information in the request buffer.

Speculative Decoding Implementation

The distributed grouped draft server supports multi-server deployment mode, with clients automatically routing to appropriate servers via request group hashing. The draft client deploys as an embedded library within the same process as the inference engine, providing zero-copy batch interfaces to minimize speculation overhead.

Frequently Asked Questions

What’s the main difference between Seer and traditional reinforcement learning systems?

Traditional systems treat multiple requests under the same prompt as integral units for scheduling, overlooking similarities between group requests. Seer fully utilizes this similarity, achieving fine-grained scheduling through divided rollout and improving speculative decoding effectiveness through group context, while maintaining full compatibility with synchronous reinforcement learning algorithms.

How does Seer improve efficiency without introducing off-policy learning?

Asynchronous reinforcement learning systems improve efficiency by allowing data generated with old policies, but this introduces off-policy learning that may affect model convergence and stability. Seer enhances efficiency by optimizing the synchronous rollout process itself, without changing the logical sequence of data generation and usage, thus maintaining algorithmic无损性.

What are Seer’s requirements for model architecture?

Seer’s design is model-architecture agnostic, applicable to both dense models and mixture-of-experts models. For different model architectures, the system precomputes different speculation token thresholds to ensure efficiency across various scenarios.

Does divided rollout introduce additional overhead?

Divided rollout indeed introduces finer-grained scheduling units, but through the global KVCache pool, it avoids recomputation overhead. Experimental results show that the performance improvement brought by this design far exceeds its minimal additional overhead.

Is Seer suitable for small-scale training?

Seer’s design primarily targets large-scale reinforcement learning training scenarios, especially those involving long-chain reasoning tasks. In small-scale tasks or those with shorter generation lengths, Seer’s advantages may be less apparent, but its core mechanisms can still provide some degree of performance improvement.

Conclusion

Seer represents a significant advancement in synchronous reinforcement learning system design. Through online context learning, it cleverly utilizes the inherent similarity between requests within groups in reinforcement learning training, addressing core bottleneck issues in the rollout phase.

The synergistic effect of three key technologies—dynamic load balancing achieved through divided rollout, long-tail latency reduction through context-aware scheduling, and inference process acceleration through adaptive grouped speculative decoding—enables Seer to significantly improve training efficiency while maintaining algorithmic无损性.

Experimental results demonstrate that Seer delivers remarkable performance improvements across models of various scales and architectures, providing more efficient infrastructure for large-scale language model reinforcement learning training. For research and production teams pursuing training efficiency without sacrificing algorithmic fidelity, Seer undoubtedly represents a technological direction worth attention.

As large language models find increasingly widespread applications in complex reasoning tasks, the demand for efficient reinforcement learning systems will only grow. Seer’s innovative online context learning approach provides a practical solution to this challenge, potentially accelerating future developments in large language models for mathematical reasoning, scientific discovery, and complex problem-solving domains.