Seer: Optimizing Synchronous LLM RL

• The paper introduces Seer, an online system that optimizes synchronous LLM reinforcement learning using divided rollout, context-aware scheduling, and adaptive grouped speculative decoding.
• It achieves up to 97% improvement in token throughput and reduces long-tail latency by up to 93% by addressing workload imbalance and resource fragmentation.
• Seer’s modular design integrates granular task decomposition, efficient KV-cache management, and dynamic scheduling to ensure rapid, on-policy reinforcement learning rollouts.

Seer is an online context learning system for fast synchronous LLM reinforcement learning whose primary objective is to address the performance bottlenecks and inefficiencies inherent in synchronous RL rollouts. In such settings, the rollout phase dominates iteration time and is often constrained by workload imbalance, long-tail latency, and suboptimal resource utilization. Seer introduces a cohesive set of techniques—divided rollout, context-aware scheduling, and adaptive grouped speculative decoding—that collectively result in substantial improvements in rollout throughput (74% to 97%) and long-tail latency reduction (75% to 93%) compared to established synchronous RL systems using vLLM (Qin et al., 18 Nov 2025).

1. Motivation and Challenges in Synchronous LLM RL

Synchronous RL for LLMs necessitates generating multiple model responses (“trajectories”) in parallel, conditioned on fresh policy weights after every parameter update (on-policy regime). Existing systems typically employ grouped rollout (GRPO), batching $G$ requests per prompt and running synchronous inference to produce all samples for a batch before proceeding to the next RL phase. This leads to two major bottlenecks:

• Workload Imbalance: Due to divergence in request output lengths and generation patterns, some instances complete significantly earlier than others, stranding compute resources and prolonging overall runtime (the “straggler” or “long-tail” problem).
• Resource Fragmentation: KV-cache preemptions and poorly aligned batch sizes result in compute and memory underutilization, with prefill and context window management adding further inefficiency.

These issues drive demand for new system-level solutions that fully address both compute/memory bottlenecks and the unpredictability of long-tail rollouts, without sacrificing strict on-policy semantics required for RL.

2. Core Techniques in Seer

Seer’s design centers on three synergistic mechanisms that collectively optimize rollout efficiency.

A. Divided Rollout

• Granular Decomposition: Each prompt group $g$ with $G$ responses is decomposed to $G$ single-response requests, and every individual request $r$ is further partitioned into chunks of $C$ tokens (e.g., 8k).
• KV-cache Optimization: Tracking and managing each sub-request’s KV-cache footprint independently avoids preemption and maximizes concurrency without memory oversubscription.
• Dynamic Dispatch: For each cycle, an active sub-request $r^\star$ is selected and dispatched to an inference instance $i^\star$ with adequate free KV capacity. After completion, its state is updated and it is either returned to the buffer or finalized, allowing seamless migration of KV states across GPUs using a global KV pool.
• Concurrency Throttling: Seer throttles in-flight requests if all instances are saturated, ensuring prefill recomputations remain rare.

B. Context-Aware Scheduling

• Online Length Probing: A designated “speculative request” in each group $g$ is used to estimate expected output lengths ($\widehat L_g$) after generation of an initial chunk. This informs scheduling by exposing potentially long-tail requests early.
• Dual-Queue Policy: High-priority queue $\mathsf Q_{\mathrm{spec}}$ manages speculative probes using shortest-generated-so-far (SFS), and a candidate set $\mathsf C_{\mathrm{rest}}$ is scheduled using largest-first (LFS) on $\widehat L_g$.
• Objective: Directly maximize total token throughput

$$TP = \frac{\sum_{g,i} |y_{g,i}|}{T_{\mathrm{rollout}}}$$

while minimizing tail-latency

$T_{\mathrm{tail}} = t_N - t_{0.9}$

where $t_N$ is the last finish time, and $t_{0.9}$ the 90th percentile.

C. Adaptive Grouped Speculative Decoding

• Distributed Grouped Draft Server (DGDS): For each group $g$, a compressed suffix tree (CST) is maintained, efficiently aggregating all tokens generated so far across any member of $g$.
• Multipath Local Speculation: Inference instances generate up to $k$ candidate continuations of at most $s$ tokens each per decoding step via the CST. Each speculative path is accepted only if verified token-by-token; on error, roll back is performed.
• Adaptive Control of Speculation Window: The speculative draft horizon $s$ is dynamically adjusted to balance amortized compute versus verification overhead, optimizing for speedup

$$\mathrm{Speedup}(s) \approx 1 + \frac{\alpha_s (s-1)}{C_{\mathrm{model}}}$$

where $\alpha_s$ is expected accepted length, and $C_{\mathrm{model}}$ is the model’s per-token decode cost.

• Trade-off Parameterization: Confidence threshold $\tau$ modulates CST path score acceptance; higher $\tau$ increases speculative match confidence but can limit speedup if set too aggressively.

3. Architecture, Formalization, and Integration

Seer’s system architecture is modular, emphasizing separation of state management, context evaluation, and inference control:

• Request Buffer: Centralized buffer with all active sub-requests and metadata (group ID, token counters, memory usage).
• Context Manager: Maintains speculative probe state, historical and online token length estimates, and applies SFS/LFS scheduling.
• Inference Engine Pool: Collection of vLLM-based inference instances with KV-cache pooling across GPU and SSD tiers; supports zero-copy data migration and direct batched speculation inputs.
• DGDS and Draft Client: DGDS acts as an external CST store per group, with clients fetching incremental updates and managing TTL-based pruning.

Integration into the RL loop is seamless: after model weight updates, Seer performs rollout, asynchronously transfers generated trajectories to a reward server (for LLM-judge or rule-based evaluation), and then triggers the next policy update phase—guaranteeing strict on-policy sampling.

4. Experimental Findings and Ablation Analyses

Empirical studies deployed Seer on clusters up to 256 H800 GPUs, targeting production-scale RL for math and vision+language LLMs (Moonlight, Qwen2-VL-72B, Kimi-K2). Key results include:

• Throughput Gains: Seer achieves +74% (Moonlight), +90% (Qwen2-VL), +97% (Kimi-K2) improvement in end-to-end token throughput relative to veRL.
• Tail Latency Reduction: 75–93% reduction across tasks, attributed primarily to context-aware scheduling and fine-grained task decomposition.
• Component Contribution: Ablation experiments demonstrate incremental gains from each technique:

Technique	Throughput Multiplier (Moon/Qwen/Kimi)
Baseline (veRL)	1.00 / 1.00 / 1.00
+ Divided Rollout	1.27 / 1.31 / 1.35
+ Context-Aware Scheduling	1.33 / 1.44 / 1.47
+ Adaptive Grouped SD	1.77 / 1.87 / 1.77

• Context Scheduling Efficacy: Online length probes reach 95% of the performance of hypothetical “oracle” LFS scheduling (perfect length foresight).
• Grouped Speculative Decoding: Yields +30% throughput versus no speculation, +19% if group context is disabled, and only +3% if adaptivity is removed, highlighting the necessity of both grouping and dynamic tuning.
• Long-Tail Mitigation: Mean speculative acceptance grows from ~1.7 to 3.5 tokens in later-stage rollouts as more group context accumulates.

5. Design Trade-offs and Limitations

While Seer’s improvements are substantial, several trade-offs and operational considerations are articulated:

• Scheduling Overhead: Approximately 1–2% of total rollout time.
• CST Memory Growth: Primarily a function of token history per group, bounded by maximum token length and time-to-live.
• Pattern Sensitivity: Effectiveness of adaptive grouped decoding correlates with intra-group sequence similarities. Adversarial or highly heterogeneous outputs can diminish speculative gains.
• KV-Cache Pooling Limits: Global pooling amortizes migration cost, but could become a system bottleneck in highly fragmented or extremely large-scale runs.

A plausible implication is that the magnitude of Seer’s advantage is task- and model-dependent, particularly sensitive to workload heterogeneity and prompt grouping granularity.

6. Future Directions

Potential extensions identified include:

• Asynchronous and Partially On-Policy RL: Relaxing on-policy constraints to further increase resource utilization and refresh cycles.
• Generalization to Large-Group or Curriculum RL: Extending divided rollout and context scheduling to settings involving significantly larger $G$ (e.g., Knapsack RL) or adaptive curriculum construction.
• Integration with Other Decoding Methods: Augmenting or combining DGDS with established decoding acceleration techniques, such as suffix decoding and n-gram caching, especially for multi-modal or inference-only applications.
• Inference-Only Large-Batch Serving: Leveraging Seer’s scheduling strategies outside RL for aggregate API serving to systematically mitigate long-tail outliers.

Collectively, Seer establishes a new paradigm for synchronous LLM RL rollout optimization by employing dynamic, context-sensitive, and fine-grained task decomposition, with demonstrated scalability to current state-of-the-art generative models (Qin et al., 18 Nov 2025).