Individual skill
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.
backend/cli/skills/ml-inference/miles/SKILL.md
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# 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
### Hardware Requirements
- **Minimum**: 4x H100 80GB (320 GB total VRAM) for 7B models
- **Recommended**: 8x H100 80GB (640 GB total VRAM) for 70B+ models
- **For 1TB+ MoE models**: Multi-node H200 clusters with InfiniBand
- **Storage**: NVMe with 10+ TB for checkpoints
> **Cost Warning**: miles workloads are expensive. A single 8x H100 node costs ~$25-50/hr.