Story · arXiv (via papers.cool)
Agent Lightning v1.0: Towards Harnessed Agentic RL (arXiv (via papers.cool))
paper · Story page
A roughly 3,500-line framework for RL where the deploy-time agent harness keeps owning tools, context, and the environment loop while the trainer observes only LLM request-response pairs. It's built to surface the failure points: retokenization, sample merging, loss normalization, backend scheduling.
In plain words
- Researchers released Agent Lightning version 1.0 for training agents, artificial intelligence systems that take actions on their own.
- The surrounding software keeps control of the tools, background information, and sequence of steps.
- The training system watches only the questions sent to the language system and its replies.
- It exposes trouble spots such as rebuilding text for training, combining examples, comparing results, and scheduling computing work.
- Researchers get a compact place to study why training becomes effective, ineffective, or unstable.
Appeared in
- Malicious skills hijack agents mid-task, and debate training curbs reward hacking
Aug 20, 2026 · in the sections
Subscribe
Get the brief in your inbox
Pick daily, weekly, or both. Nothing is gated either way: every issue is on the site and in the feeds.
- Weekdays at 8:45am IST, one lead story and 6 to 9 items.
- Sundays, an argued synthesis rather than a recap.
- One click to leave, and quiet days say so in the subject line.