Show HN: I built an integration for RL training of browser agents for everyone

Posted by filtr12 on Wednesday March 25, 2026 @01:11PM
from the show-hn dept.

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.

( 2 comments | 5 points | on HN )

Re:Show HN: I built an integration for RL training of browse... by nithisha2201 on Wednesday March 25, 2026 @03:14PM (#47521868)

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.

[ Parent | on HN ]

Re:Show HN: I built an integration for RL training of browse... by georaa on Wednesday March 25, 2026 @01:21PM (#47520371)

Browser agents are the use case where RL makes the most sense - the reward signal is obvious (did the task get done or not) and the action space is bounded. Curious how you handle the credit assignment problem across multi-step navigation though.

[ Parent | on HN ]