Agent skill · research science · davila7
miles-rl-training
Provides guidance for enterprise-grade RL training using miles, a production-ready fork of slime. Use when training large MoE models with FP8/INT4, needing train-inference alignment, or requiring speculative RL for maximum throughput.
Why this skill is useful
Adds executable scripts and detailed workflows for training large MoE models with specific optimizations that aren't widely documented.
What it needs
Requires docker, ray, sglang-router, torch, transformers installed locally. About 4k tokens when loaded. Last updated 2026-08-06. 30,138 stars on the source repository.
What this skill does
miles: Enterprise-Grade RL for Large-Scale Model Training miles is a high-performance, enterprise-ready RL framework optimized for large-scale model post-training. Built as a production fork of slime, it addresses critical challenges in MoE training stability, low-precision training, and train-inference alignment. When to Use miles Choose miles when you need: Training 1TB+ MoE models (DeepSeek V3, Qwen3-MoE) FP8 or INT4 quantization-aware training Bit-wise identical train-inference alignment Speculative RL for maximum throughput Production stability with enterprise support Consider alternatives when: You want the research-grade original → use slime You need flexible backend swapping → use verl You want PyTorch-native abstractions → use torchforge Key Features Low-Precision Training Unified FP8: End-to-end FP8 for both inference and training INT4 QAT: 1TB models on single-machine VRAM (H200) Rollout Routing Replay (R3): Bit-wise expert alignment for MoE Performance Optimizations Speculative RL: 25%+ rollout speedup with online SFT draft models Zero-Copy Weight Sync: CUDA IPC zero-copy mapping Partial Rollout: Recycle half-finished trajectories Train-Inference Alignment TIS/MIS: Truncated/Masked Importance Sampling for off-policy correction Kernel-level optimization: FlashAttention-3, DeepGEMM integration Installation Quick Start miles inherits slime's configuration system. Basic training: --- Workflow 1: Large MoE Training Use this workflow for training large MoE models like DeepSeek V3 or Qwen3-MoE. Prerequisites Checklist [ ] H100/H200 GPUs with FP8 support [ ] MoE model (DeepSeek V3, Qwen3-MoE) [ ] Docker environment with miles Step 1: Environment Setup Step 2: Configure Training Verification Checklist [ ] Model loads without errors [ ] Routing decisions are consistent [ ] No NaN/Inf in loss values --- Workflow 2: Speculative RL Training Use this workflow for maximum rollout throughput with EAGLE speculative decoding. How Speculative RL Works 1. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills davila7/post-training-miles