19 August 2026

Miles v0.1 open-source tool enables large-scale AI model improvement

• Miles v0.1 is an open system for improving AI models after initial training through reinforcement learning, a technique where models learn by trial and error.
• The system handles multiple technical challenges simultaneously: running parallel experiments, isolating code safely, training asynchronously, and working across different hardware setups.
• The project involved 72 contributors working over 9 months and has already been tested on multiple commercial AI models including DeepSeek and Qwen.

Where they differ

TLDR AI emphasized Miles as a scalable technical system for post-training workflows, while Latent Space stressed its maturity as battle-tested infrastructure built by a substantial community.

TLDR AITLDR editorial team

Miles v0.1 is an open system for improving AI agents through post-training reinforcement learning. It packages rollout, sandboxing, asynchronous training, and multi-hardware support to run improvement loops at scale without stopping the pipeline.

Latent Spaceswyx & Alessio

Miles, an open-source RL framework built over 9 months with 72 contributors and battle-tested on multiple models including Kimi K3, DeepSeek V4, and Qwen 3.8, represents serious infrastructure for LLM and multimodal model training.