Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ihatesea69/HieuNghi-AI-Skills --skill milesgit clone --depth 1 https://github.com/ihatesea69/HieuNghi-AI-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ihatesea69/hieunghi-ai-skills/miles)<a href="https://agentmods.dev/skills/ihatesea69/hieunghi-ai-skills/miles"><img src="https://agentmods.dev/badge/skills/ihatesea69/hieunghi-ai-skills/miles/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ihatesea69/hieunghi-ai-skills/miles"><img src="https://agentmods.dev/badge/skills/ihatesea69/hieunghi-ai-skills/miles.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00051 | $0.02424 |
| Opus 5 | $0.00026 | $0.01212 |
| Sonnet 5 | $0.00010 | $0.00485 |
| Haiku 4.5 | $0.00005 | $0.00242 |
Grade A, and why
miles-rl-training scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to miles-rl-training — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 316 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
# Recommended: Docker
docker pull radixark/miles:latest
docker run --rm --gpus all --ipc=host --shm-size=16g \
-it radixark/miles:latest /bin/bash
# From source
git clone https://github.com/radixark/miles.git
cd miles
pip install -r requirements.txt
pip install -e .
Quick Start
miles inherits slime's configuration system. Basic training:
python train.py \
--advantage-estimator grpo \
--model-name qwen3-30b-a3b \
--hf-checkpoint /path/to/qwen3-30b-a3b-hf \
--rollout-batch-size 512 \
--n-samples-per-prompt 8
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 316 lines · 51 tokens per session scan A cbf610faa37b
miles-rl-training is a skill published in the GitHub repository ihatesea69/HieuNghi-AI-Skills (3 stars, last pushed 6mo ago), licensed MIT. It adds 51 tokens to every session and 2,424 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to miles-rl-training, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
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.
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.
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.
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.
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.
slime-rl-training
Provides guidance for LLM post-training with RL using slime, a Megatron+SGLang framework. Use when training GLM models, implementing custom data generation workflows, or needing tight Megatron-LM integration for RL scaling.