Moonshot AI Researchers Introduce Seer: An Online Context Learning System for Fast Synchronous Reinforcement Learning RL Rollouts
How do you retain reinforcement studying for massive reasoning fashions from stalling on a couple of very lengthy, very sluggish rollouts whereas GPUs sit beneath used? a crew of researchers from Moonshot AI and Tsinghua University introduce ‘Seer’, a brand new on-line context studying system that targets a selected programs bottleneck in reinforcement studying for massive language fashions. In synchronous on coverage setups, the rollout section dominates the price of every iteration. Seer restructures this section and reviews rollout throughput good points of 74 p.c to 97 p.c and tail latency reductions of 75 p.c to 93 p.c in contrast with a powerful synchronous baseline known as veRL.
Why synchronous rollout is sluggish for reasoning fashions?
Modern reasoning RL workloads use lengthy chain of thought type outputs. In the Seer experiments, the researchers apply GRPO to 3 completely different fashions, Moonlight, Qwen2 VL 72B and Kimi K2. These workloads run on 32 compute nodes with 8 H800 GPUs per node. The three duties use 32, 128 and 256 GPUs respectively, with 400, 600 and 800 prompts per iteration and eight or 16 responses per immediate.
Maximum era size is massive. Moonlight is configured for 65,536 tokens, Qwen2 VL 72B for 40,960 tokens and Kimi K2 for 98,304 tokens. A single lengthy chain of thought request can develop from a couple of hundred megabytes of KVCache to tens of gigabytes as decoding progresses. This reminiscence development forces situations to scale back concurrency or to preempt requests, which triggers costly re decoding.
The analysis crew defines tail requests because the final 10 p.c of requests to complete in a rollout. For Moonlight and Qwen2 VL 72B, this tail alone can eat as much as 50 p.c of the full rollout time within the baseline system. Rollout already dominates iteration time, so this tail impact immediately slows RL.
Seer structure on high of Mooncake and vLLM
Seer retains the RL algorithm an identical to synchronous veRL. Each coaching iteration makes use of solely knowledge from the present rollout iteration, so the system preserves on coverage habits. The coaching section makes use of Megatron for distributed optimization. The rollout section makes use of an in home implementation of vLLM because the inference engine.
To assist aggressive request scheduling, Seer depends on a Global KVCache Pool constructed on the Mooncake disaggregated KVCache structure utilized in manufacturing for Kimi. Mooncake supplies a two tier DRAM and SSD KV cache retailer shared throughout inference nodes, which permits Seer emigrate requests with out recomputing prefills.
On high of this substrate, Seer introduces three key mechanisms:
- Divided Rollout
- Context Aware Scheduling
- Adaptive Grouped Speculative Decoding
These are orchestrated by a Request Buffer, a Context Manager and an Inference Engine Pool linked to the Global KVCache Pool.
Divided Rollout, positive grained scheduling and migration
Conventional synchronous rollout assigns complete GRPO teams to inference situations. A gaggle is a set of requests that share one immediate. Once assigned, a gaggle stays on the identical occasion till all responses end. Due to massive variance in output lengths, this results in load imbalance and lengthy working stragglers.
Seer breaks teams down in two steps. It first decomposes every group into particular person requests. It then divides every request into a number of chunks primarily based on era size. When the scheduler dispatches a request from the Request Buffer, it units a small max tokens worth comparable to 8,000 tokens for that chunk. After every chunk, the request is re enqueued till it reaches an finish of sequence token or its unique max tokens restrict.
Because KVCache is saved within the Global KVCache Pool, divided requests can transfer between situations at chunk boundaries with out re working the prefill. The scheduler maintains a concurrency degree that retains reminiscence utilization excessive whereas avoiding preemption. This reduces waste and smooths KVCache utilization throughout the iteration.
Context Aware Scheduling utilizing group size statistics
The analysis crew observe that completely different requests in the identical group are likely to have correlated output lengths. Seer makes use of this construction as on-line context. For every immediate group, it designates one request because the speculative request. The scheduler retains speculative requests in a excessive precedence queue and serves them with a smallest first coverage primarily based on generated tokens thus far. Short requests full rapidly and exit. Long requests stay and establish teams which are potential tail candidates.
The Context Manager maintains a size estimate for every group. It updates this estimate to the utmost generated size amongst accomplished requests within the group. If no request has completed, it makes use of the unique max tokens as a conservative certain. Once speculative requests are in flight or completed, Seer schedules remaining requests with an approximate longest first coverage at group degree. This design achieves throughput and tail habits near an oracle scheduler that is aware of all output lengths upfront.
Adaptive Grouped Speculative Decoding
Seer provides Adaptive Grouped Speculative Decoding on high of the earlier two elements to speed up decoding, particularly for lengthy requests within the tail. It introduces a Distributed Grouped Draft Server, or DGDS. DGDS maintains a Compressed Suffix Tree for every group and aggregates token sequences from all requests in that group. Instances asynchronously append generated tokens to DGDS, periodically fetch up to date suffix timber and carry out native speculative decoding primarily based on the shared sample statistics.
The system adjusts draft size and the variety of paths in keeping with mannequin structure, batch dimension and measured acceptance size. For dense and Mixture of Experts fashions, it pre-computes completely different hypothesis thresholds and makes use of them to certain draft depth for every batch. In late tail phases, concurrency is low, so Seer will increase draft depth and permits multi path drafting to boost accepted tokens per step.
Ablation outcomes present that divided rollout yields as much as 35 p.c throughput enchancment over the baseline. Adding Context Aware Scheduling will increase this to as much as 47 p.c over baseline. Enabling grouped speculative decoding raises the full speedup to 77 p.c to 87 p.c over the baseline within the evaluated iteration.
End to finish influence on RL coaching
The analysis crew consider Seer on three RL duties constructed on Moonlight, Qwen2 VL 72B and Kimi K2. They run 10 rollout iterations per activity and measure output tokens per second and completion time for every rollout. Seer improves rollout throughput by 74 p.c to 97 p.c throughout these workloads relative to veRL with the identical RL algorithm and vLLM primarily based inference engine.
Tail latency is lowered by 75 p.c to 93 p.c. For reminiscence constrained duties, the baseline system spends as much as half of its time on the final 10 p.c of requests. Seer removes most of this tail by combining divided rollout, Context Aware Scheduling and Adaptive Grouped Speculative Decoding on high of the Mooncake primarily based Global KVCache Pool.
Key Takeaways
- Rollout bottleneck: Seer targets the rollout section of synchronous RL, which accounts for about 63% to 87% of iteration time and is dominated by lengthy tail requests and KV cache fragmentation.
- Three core mechanisms: Seer combines divided rollout, context conscious scheduling and adaptive grouped speculative decoding to use output size and sample similarity amongst GRPO responses that share a immediate.
- Fine grained scheduling on a worldwide KV cache: Requests are break up into chunks and migrated throughout a Mooncake type Global KVCache Pool, which preserves synchronous on coverage RL whereas protecting GPU reminiscence utilization excessive and lowering preemptions.
- Online context for tail latency discount: Group degree size statistics from speculative requests drive context conscious scheduling that approximates an oracle longest first scheduler and sharply reduces the time spent on the final 10 p.c of requests.
- Measured finish to finish good points: On manufacturing grade RL workloads with Moonlight, Qwen2 VL 72B and Kimi K2, Seer improves rollout throughput by 74% to 97% and reduces lengthy tail latency by 75% to 93% relative to a state-of-the-art synchronous vLLM primarily based baseline.
Editorial Comments
Seer is a vital programs contribution as a result of it optimizes the rollout section in synchronous RL with out altering the underlying GRPO algorithm, so it preserves on coverage ensures and reproducibility whereas fixing an actual infrastructure bottleneck. The mixture of divided rollout, context conscious scheduling and adaptive grouped speculative decoding provides a sensible template for different RL stacks that depend on lengthy chain of thought reasoning fashions and huge KVCache footprints. Overall, Seer exhibits that on-line context studying on the programs degree is now as crucial as mannequin structure for scaling reasoning RL effectively.
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