Miles Rl Training
Skill ActiveProvides 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.
To guide users in performing enterprise-grade Reinforcement Learning training for large-scale MoE models, leveraging advanced techniques like FP8/INT4 quantization and speculative RL for maximum efficiency and alignment.
Features
- Low-precision training (FP8, INT4)
- MoE model training and alignment (R3)
- Speculative RL for throughput optimization
- Train-inference alignment
- Production-ready framework guidance
Use Cases
- Training large MoE models (1TB+)
- Enabling FP8 or INT4 quantization-aware training
- Achieving bit-wise identical train-inference alignment
- Maximizing rollout throughput with speculative RL
Non-Goals
- Serving as the research-grade original slime framework
- Providing flexible backend swapping (use verl)
- Offering PyTorch-native abstractions (use torchforge)
Trust
- warning:Issues Attentionopen=17, closed=4. The ratio of open to closed issues in the last 90 days is low, suggesting maintainers may be slow to respond to or resolve issues.
Installation
npx skills add davila7/claude-code-templatesRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
Trust Signals
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