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Large-Model RL Infrastructure

Classical distributed RL systems were designed around actors, learners, replay buffers, and fast environment stepping. Modern RL for large models adds a different set of bottlenecks: long-text generation, heavyweight verifiers, frequent weight synchronization, and, increasingly, tool-using or embodied environments. As a result, the system boundary expands from a simple actor-learner loop to a larger graph that includes the trainer, rollout engine, reward service, and sometimes a separate environment pool.

This page surveys that transition and highlights the open-source systems that define the current design space.

Why Modern RL Infra Changed

Large-model RL changes the workload in three ways:

  • Generation dominates runtime: in RLHF and RLVR, the expensive step is often response generation rather than the backward pass.
  • Long trajectories create stragglers: long chain-of-thought reasoning and multi-turn agent tasks produce highly variable rollout lengths.
  • The environment got heavier: reward computation may involve unit tests, sandboxed tools, simulators, or judges instead of a scalar reward from a lightweight environment.

That pushes system design away from a single actor-learner pipeline and toward four cooperating roles:

  1. Trainer updates model parameters.
  2. rollout engine runs fast generation, often with vLLM or SGLang.
  3. Reward service scores outputs with rules, verifiers, or models.
  4. environment pool hosts tools, sandboxes, or simulators for multi-turn or embodied tasks.
graph TD
    T[Trainer<br/>parameter updates] -->|weights| R[`rollout` Engine<br/>vLLM / SGLang]
    R -->|samples| RW[Reward Service<br/>rules / verifiers / RM]
    R -->|tool calls / actions| E[`environment pool`<br/>sandboxes / simulators]
    E -->|observations / results| R
    RW -->|scored trajectories| T

Evolution of Systems

1. Tightly Coupled RLHF

Early RLHF stacks such as DeepSpeed-Chat and TRL were effective for small-to-medium models, but they kept training and generation tightly coupled. That simplicity helped experimentation, yet it also meant that KV cache pressure, model parallelism, and control flow were all handled inside one training-oriented process.

This style remains useful for learning, for small experiments, and for settings where the policy model and reward pipeline still fit comfortably into one cluster layout.

2. Hybrid and disaggregated Rollout

The next step was to split training from high-throughput generation. Systems such as OpenRLHF, veRL (HybridFlow), and NeMo-RL made this separation practical by pairing large-model training backends with specialized inference engines.

The key idea is simple: the trainer and the rollout engine have different performance goals, so they should not be forced into the same execution model.

  • The trainer wants sharding efficiency, optimizer state management, and stable gradient throughput.
  • The rollout engine wants fast decoding, efficient KV-cache management, and dynamic batching.

This is the point where disaggregated execution became a core systems concept rather than an implementation detail.

3. fully async Agentic RL

Once the workload shifted toward long reasoning traces and multi-turn tool use, synchronous systems began to leave too much hardware idle. Modern stacks such as AReaL, slime, NeMo-RL async GRPO, and ROLL / RollArt move further toward fully async execution:

  • trainers continue updating while old requests are still finishing,
  • rollout workers receive periodic weight refreshes,
  • reward evaluation overlaps with training,
  • and the environment lifecycle is treated as a first-class systems problem.

This is especially important for agentic coding or verifier-heavy tasks, where sandbox startup, test execution, or external tools may dominate end-to-end latency.

graph LR
    A[Tightly Coupled RLHF<br/>trainer + generation in one stack] --> B[Hybrid / `disaggregated` RL<br/>trainer separated from `rollout`]
    B --> C[`fully async` Agentic RL<br/>training, reward, and environment overlap]
    C --> D[Embodied / Simulator-Aware RL<br/>simulation becomes part of infra]

Framework Matrix

Framework Training Backend rollout Backend Sync / Async Agentic Multi-Pool Resources Best Fit
OpenRLHF DeepSpeed ZeRO-3 / FSDP vLLM Sync + async option Yes Partial Practical RLHF and RLVR on large LLMs
veRL (HybridFlow) FSDP / Megatron-LM vLLM / SGLang Mostly sync, async experiments Yes Yes Flexible research on large-model RL pipelines
NeMo-RL DTensor / Megatron-Core vLLM / Megatron Inference Sync + async GRPO Yes Yes Industrial-scale Megatron-based training
slime Megatron-LM SGLang + router Sync + fully async Yes Yes Large agentic training with explicit rollout routing
AReaL Megatron / FSDP SGLang / vLLM fully async Yes Yes Long-CoT and agent-first RL research
ROLL / RollArt Megatron / FSDP2 SGLang / vLLM fully async Yes Strong heterogeneity Agentic RL with hardware-aware scheduling
RLinf Megatron + DTensor SGLang Sync / async, simulator-aware Embodied-first Yes + simulation pool VLA and embodied RL with simulators

Three reading heuristics are useful here:

  • OpenRLHF / veRL / NeMo-RL define the mainstream large-model RL training pattern.
  • AReaL / slime / ROLL show how systems evolve when long trajectories and agent environments dominate.
  • RLinf extends the same systems logic into embodied RL, where the simulator becomes part of the infrastructure rather than just a data source.

How to Choose

Classical distributed RL

If your workload is still based on vectorized Gym-style environments, replay buffers, or standard control benchmarks, start with the frameworks on Frameworks & Systems. They are simpler and better aligned with that problem shape.

Multi-machine LLM RL

If you need practical RLHF or RLVR on large language models, start with OpenRLHF, veRL, or NeMo-RL. They cover the main design choices around weight sync, inference backends, and large-model parallelism without forcing you into a highly specialized agent stack from day one.

Long CoT and agentic workloads

If rollout length variance, verifier cost, or tool orchestration is the real bottleneck, look at AReaL, slime, or ROLL. Their value is not just higher throughput, but better handling of stale weights, asynchronous pipelines, and heavy environment services.

Embodied and VLA training

If the policy interacts with simulators or robot-centric environments, the relevant question is no longer just trainer-versus-generator separation. You also need to reason about simulation, rendering, and embodied data services. That is the setting where RLinf becomes the most natural bridge from Part IV to Part III: Embodied AI.

Future Direction

The emerging pattern is a three-pool design:

  1. training pool for optimizer-heavy updates,
  2. rollout pool for decode-heavy inference,
  3. environment pool for tools, verifiers, or simulators.
graph LR
    subgraph TP[training pool]
        T1[optimizer state]
        T2[gradient updates]
    end

    subgraph RP[`rollout` pool]
        R1[prefill / decode]
        R2[KV-cache management]
    end

    subgraph EP[`environment pool`]
        E1[tool sandboxes]
        E2[verifiers / simulators]
    end

    TP -->|weight sync| RP
    RP -->|trajectories| TP
    RP -->|actions / calls| EP
    EP -->|results / observations| RP

This direction matters because each pool stresses different hardware and different scheduling logic. The main systems challenge is no longer only distributed optimization; it is coordinating parameter movement, trajectory freshness, and heterogeneous execution across those pools.

For large language models, this trend points toward more asynchronous weight updates, lighter synchronization, and better overlap between generation and evaluation. For embodied systems, it points toward tighter coupling between distributed RL and the simulation stack discussed in Part III.

References