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RadixArk published Miles on the PyTorch official blog on June 30, 2026. The framework is open-source, forked from THUDM’s slime project, and designed to close the gap between research-scale RL experiments and production post-training runs on frontier-class models.

The announcement is significant because it appears on PyTorch’s own blog — not a startup’s Medium post or a GitHub readme. That signals the PyTorch community endorsing Miles as a reference architecture for large-scale LLM RL training.

What Problem Miles Solves

Reinforcement learning post-training has become the dominant technique for capability improvement beyond base model pretraining. RLHF, RLAIF, and variants like GRPO and DPO have produced the reasoning gains in DeepSeek-R1, Kimi K2-series, and Qwen3. But running RL at the scale those models were trained on is a distributed systems problem, not just an ML problem.

The existing options before Miles:

Framework Where it excels Where it breaks
TRL (HuggingFace) Single-node fine-tuning, fast iteration Doesn’t scale past a few GPUs efficiently
veRL Multi-node RL, PyTorch-native Wraps Megatron-LM as black box; limited MoE support
OpenRLHF Clean pipeline, good documentation Less focus on week-long production fault tolerance
slime Research-first, rapid iteration Not production-hardened; no enterprise operational tooling

Miles sits at the intersection of research flexibility and production reliability — its explicit design goal.

Architecture: Four Components, One Trainer

Miles composes four established tools rather than reinventing them:

┌─────────────────────────────────────────┐
│              Your reward function        │
│              Your RL algorithm           │
│              Your model spec             │
└──────────────────┬──────────────────────┘
                   │ Miles trainer (small core)
         ┌─────────┴──────────┐
         │                    │
    SGLang              Megatron-LM
  (rollout)             (training)
         │                    │
         └─────────┬──────────┘
                   │
               Ray + PyTorch
       (orchestration + tensors)

SGLang handles rollout: generating samples from the current model policy at high throughput. Miles uses SGLang’s continuous batching and prefix caching to maximize GPU utilization during the generation phase.

Megatron-LM handles training: applying gradient updates with tensor parallelism, pipeline parallelism, and sequence parallelism across large GPU clusters. Unlike frameworks that wrap Megatron as a black box, Miles plugs directly into Megatron’s argument parser, parallelism primitives, and checkpoint format.

Ray provides orchestration: actor placement, supervision, fault recovery, and the async communication channel between rollout and training stages.

PyTorch is the native layer throughout: models are standard torch.nn.Module instances, losses are regular autograd graphs, no intermediate abstractions that require their own debugging tools.

The MoE Problem Miles Was Built to Solve

Most frontier models today are Mixture-of-Experts (MoE) architectures: DeepSeek-V3/V4, Kimi K2, Qwen3.5-235B, GLM-5. MoE models route tokens through a subset of expert layers, and those routing decisions are state-dependent.

The problem: during RL post-training, the rollout phase (SGLang) and the training phase (Megatron-LM) must agree on how tokens were routed. If rollout uses one routing decision and training uses a different one, gradient updates don’t reflect the actual generation behavior — introducing training instability.

Miles solves this with Rollout Routing Replay: the routing decisions from rollout are recorded and replayed during training, ensuring the training gradient is computed on the same computational path that produced the sample. For MoE models, this matters. For dense models, it’s a no-op.

Key Technical Features

Asynchronous Rollout

In most RL training pipelines, rollout and training are synchronized: generate a batch, train on it, generate the next batch. Miles breaks this coupling.

In async mode, rollout actors stream samples to a queue independently of the training step. Training reads from the queue when ready. This decouples the two phases so neither waits on the other — useful when rollout and training have different GPU requirements or when rollout is I/O-bound (tool calls, retrieval, environment interaction).

Fast Weight Synchronization

After each training step, the updated model weights must be transferred back to the rollout actors. Miles uses NCCL and RDMA channels for tensor movement; Ray handles only the control path. This keeps weight sync at interconnect speeds rather than slower communication channels.

Fault Tolerance for Week-Long Runs

A single RL post-training run on a frontier model can take 7–14 days across hundreds of GPUs. Hardware failures are not edge cases at that scale — they’re expected.

Miles uses Ray’s actor supervision model: each component (rollout, training) is a Ray actor with a supervisor that can restart failed ranks. It supports rank-level recovery so a partial GPU failure doesn’t abort the entire run.

Low-Precision Recipes

Miles ships unified training recipes for BF16, FP8, MXFP8, and INT4-QAT (quantization-aware training). These are increasingly important: FP8 training on Hopper GPUs delivers ~1.5–2× throughput improvement over BF16 with minimal accuracy loss when done correctly. Miles provides the recipe so teams don’t have to implement precision-level alignment between SGLang and Megatron themselves.

Extension Points (Small Core Philosophy)

The trainer stays intentionally small. Customization happens through Python modules:

  • Rollout functions — replace the generation loop for custom sampling behavior
  • Reward functions — plug in any process reward model, verifier, or environment
  • Loss functions — new RL objectives (GRPO, DPO, RLOO variants) without forking
  • Sample filters — reject or reweight samples before training
  • Training hooks — metrics, diagnostics, auxiliary losses
  • Model specs — architecture-specific PyTorch modules (custom attention, routing, etc.)

Supported Models (Ready-to-Run Recipes)

Miles ships pre-configured recipes for:

Model Org Architecture
DeepSeek-V4 DeepSeek MoE
Kimi K2.5 / K2.6 Moonshot AI MoE
GLM-5 / GLM-5.1 Zhipu AI (THUDM) Dense + MoE variants
Qwen3.5 / Qwen3.6 Alibaba MoE
Llama 4 variants Meta Dense + MoE

Target hardware: NVIDIA Hopper (H100/H200) and Blackwell (B200/GB200) GPUs.

Miles vs. slime: The Fork Relationship

Miles is forked from THUDM/slime and explicitly designed to co-evolve with it rather than diverge. The relationship matters for teams evaluating both:

slime is the research-first upstream: fast iteration, clean interfaces, focused on algorithm exploration. THUDM (the GLM team) uses it internally and it reflects research velocity over operational stability.

Miles adds the production layer on top: deeper SGLang integration, TITO (training-time inference-time optimization), operational tooling for cluster management, support for new models and hardware as they release, and the enterprise-facing documentation and stability commitments.

Teams doing algorithm research should likely start with slime. Teams operating post-training pipelines in production should start with Miles.

Who Should Evaluate Miles

Evaluate Miles if:

  • You’re running RL post-training on MoE frontier models (DeepSeek, Kimi, Qwen, GLM variants)
  • Training runs last more than a few hours (fault tolerance matters)
  • Your team has GPU clusters with RDMA interconnects
  • You need to customize reward functions or RL algorithms without forking the framework
  • You’re on NVIDIA Hopper or Blackwell hardware

Miles is probably not the right fit if:

  • You’re fine-tuning a 7B–13B model on a single node (use TRL, Axolotl, or Unsloth)
  • You’re using LoRA for parameter-efficient fine-tuning on consumer GPUs (same)
  • Your team doesn’t have distributed systems experience (Miles assumes cluster-scale fluency)
  • You need a model not in the supported recipe list (you can add it via model spec extension points, but there’s no recipe shortcut)

Builder Checklist

  • Confirm your target model has a Miles recipe or plan your model spec extension
  • Verify cluster has RDMA support — NCCL over RDMA is Miles’s assumed fast path
  • Choose between async rollout (throughput-optimized) and sync (simpler debugging) based on your environment
  • If using MoE model: enable Rollout Routing Replay from the start, not as an afterthought
  • Pin your SGLang and Megatron-LM versions to the ones in Miles’s tested matrix
  • Set up Ray fault tolerance checkpointing before starting multi-day runs
  • Check upstream slime changelog for algorithm improvements you may want to pull forward

Resources