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miles-rl-training

Optimize large-scale MoE reinforcement learning training with Megatron parallelism and FP8 or INT4 quantization.

Updated May 4, 2026
Search Tags:#fp8#rl-training#sglang#megatron#moe#int4#speculative-decoding
One-click install
npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill miles-rl-training-supporter09
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: miles-rl-training
Source: https://github.com/Supporter09/Face_Anti_Spoofing_Biometric/tree/main/.claude/skills/miles
Command: npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill miles-rl-training-supporter09

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps teams run large-scale reinforcement learning training for modern MoE language models without getting lost in brittle configuration, unstable low-precision setups, or train-inference mismatch.

Core Features & Use Cases

  • Enterprise MoE Training: Plan and configure high-throughput training for large expert models with Megatron-style parallelism.
  • Low-Precision Workflows: Apply FP8 and INT4 strategies when memory pressure and throughput are critical.
  • Train-Inference Alignment: Use routing replay, deterministic settings, and validation checks to keep rollout behavior consistent with training.
  • Speculative RL: Speed up rollout generation with draft-model speculation and online updates.
  • Troubleshooting Support: Diagnose collapse, routing mismatch, quantization regressions, and synchronization issues with practical recovery steps.

Quick Start

Ask the Skill to create a miles training plan for your MoE model, including alignment, precision settings, rollout acceleration, and the safest configuration choices for your hardware.

Frequently Asked Questions about miles-rl-training

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

FAQPage Schema
How do I configure large-scale reinforcement learning training for MoE language models?▼

Configure large-scale RL training for MoE models by applying Megatron-style parallelism, routing replay, and train-inference alignment to stabilize high-throughput enterprise post-training workflows.

What is the best way to maintain train-inference alignment during SGLang rollout integration?▼

Maintain train-inference alignment during SGLang rollout integration by using deterministic expert-routing support, routing replay, and validation checks to keep rollout behavior consistent with training.

Can I use FP8 or INT4 quantization for MoE reinforcement learning training?▼

You can apply FP8 and INT4 quantization strategies to MoE reinforcement learning training when memory pressure and throughput are critical for your enterprise post-training setup.

How does speculative decoding speed up rollout generation in RL training?▼

Speculative decoding speeds up rollout generation in RL training by using draft-model speculation and online updates to accelerate generation without compromising training stability.

Why does expert routing collapse or mismatch during low-precision MoE training?▼

Expert routing collapse or mismatch during low-precision MoE training occurs from quantization regressions and synchronization issues, requiring deterministic settings and practical recovery steps to diagnose.

Do I need validated checkpoint conversion for Megatron parallelism in RL training?▼

Validated checkpoint conversion is required for Megatron parallelism in RL training to ensure miles-compatible configuration and deterministic expert-routing support across distributed setups.

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