Show HN: I built an integration for RL training of browser agents for everyone(github.com)
github.com
Show HN: I built an integration for RL training of browser agents for everyone
https://github.com/PrimeIntellect-ai/verifiers/tree/main/verifiers/envs/integrations/browser_env
This integration allows for scalable evals and training of browser agents with hosted Prime Intellect eval + training pipelines and headless browser infrastructure on Browserbase to RL train browser agents with LoRA.
3 comments
Interesting, how do you handle the observability side during training? One thing I ran into with multi-agent RL is that reward signals alone don't tell you much about why an agent is failing. Curious if you've built any tooling around that.