radixark/miles
Miles is an enterprise-facing reinforcement learning framework for LLM and VLM post-training, forked from and co-evolving with slime.
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SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
4 releases in the last 90 days, the latest 2 days ago, with 131 contributors on GitHub.
Use Cases & Benefits
- Provides an enterprise-grade reinforcement learning framework for post-training large-scale language and vision models.
- Delivers high-throughput, fully asynchronous RL with precision, stability, and observability at trillion-parameter scale.
- Use for fine-tuning frontier large language models with low-precision training and multi-GPU scalability.
- Use for deploying agentic environments to train coding and computer-use agents with customizable rollout layers.
- Use for integrating day-0 support for new multimodal models and hardware platforms in production RL workflows.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Code of conduct: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
Miles is a high-performance, enterprise-ready reinforcement learning framework for large-scale model post-training. It pairs SGLang for high-throughput rollout with Megatron-LM for scalable training, and ships the precision, stability, and observability features an RL run needs at trillion-parameter scale. A PyTorch FSDP2 backend is available for runs that would rather train the HuggingFace implementation as-is, though the recipes, the parallelism, and the largest models all live on Megatron-LM. See Training Backends.
"A journey of a thousand miles begins with a single rollout."
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
+695 stars over 52 days across 10 snapshot days; best day +115 on Sep 5.
Forks
577+136
Open issues
864-91
Compared with peers in LLM
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| thedotmack/claude-mem | 113.6K | +8.8K | 35 | today |
| NVIDIA/auto-ontology | 7.5K | +5.6K | 37 | — |
| HKUDS/DeepTutor | 55.5K | +5.5K | 65 | 5 days ago |
| affaan-m/ECC | 297.9K | +5.2K | 63 | 35 days ago |
| DietrichGebert/ponytail | 185.3K | +5.1K | 60 | 7 days ago |
| radixark/miles | 3.2K | — | 60 | 2 days ago |
| Category median | 113.6K | +5.5K | 60 | — |
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