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miles-rl-training

Trains large MoE models with FP8/INT4 quantization and R3 alignment.

Updated Jan 12, 2026
Search Tags:#enterprise#reinforcement learning#fp8#sglang#megatron-lm#moe#int4
One-click install
npx skills add https://github.com/MesferAli/XCircle --skill miles-rl-training-mesferali
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: miles-rl-training
Source: https://github.com/MesferAli/XCircle/tree/main/.claude/skills/miles
Command: npx skills add https://github.com/MesferAli/XCircle --skill miles-rl-training-mesferali

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires sglang-router>=0.2.3, ray, torch>=2.0.0, transformers>=4.40.0, and includes references (resource) components.

What problem does it solve?

This Skill addresses the complexities of training large-scale, enterprise-grade Mixture-of-Experts (MoE) models, particularly those requiring advanced techniques like FP8/INT4 quantization, train-inference alignment, and speculative RL for maximum throughput.

Core Features & Use Cases

  • Low-Precision Training: Supports unified FP8 and INT4 Quantization-Aware Training for massive models.
  • Train-Inference Alignment: Ensures bit-wise identical alignment between training and inference using techniques like Rollout Routing Replay (R3).
  • Performance Optimizations: Features speculative RL for increased throughput and efficient weight synchronization.
  • Use Case: Train a 1TB+ MoE model like DeepSeek V3 or Qwen3-MoE using FP8 quantization, ensuring that the trained model behaves identically during inference as it did during training, all while maximizing training speed.

Quick Start

Use the miles-rl-training skill to train a Qwen3-30B model using GRPO with a specified HuggingFace checkpoint and rollout batch size.

Frequently Asked Questions about miles-rl-training

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I train large Mixture-of-Experts models with FP8 quantization?▼

This Skill enables enterprise Reinforcement Learning for large MoE models by supporting unified FP8 and INT4 Quantization-Aware Training, allowing you to train massive models exceeding 1TB efficiently.

What is train-inference alignment for MoE models and how does it work?▼

Train-inference alignment ensures bit-wise identical behavior between training and inference for MoE models. It works using Rollout Routing Replay (R3) to maintain consistent expert routing and outputs across both phases.

Can I train a 1TB MoE model like DeepSeek V3 using GRPO?▼

Yes, you can train 1TB+ MoE models like DeepSeek V3 using GRPO. The Skill supports enterprise-scale Reinforcement Learning with specified HuggingFace checkpoints and configurable rollout batch sizes.

Does SGLang work with reinforcement learning training for large MoE models?▼

Yes, SGLang is integrated as a core dependency to support reinforcement learning training for large MoE models, facilitating efficient weight synchronization and train-inference alignment during the RL process.

How do I increase RL training throughput for large language models?▼

To increase RL training throughput for large language models, this Skill implements speculative RL and efficient weight synchronization, significantly maximizing training speed for massive MoE architectures.

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