Show HN: LlamaGym – fine-tune LLM agents with online reinforcement learning
1–10 of 29 posts
Re: Show HN: LlamaGym – fine-tune LLM agents with online reinforcement learning
#2Interesting project, basically a wrapper too around openai gym-like functionality that can handle open llms.
Re: Show HN: LlamaGym – fine-tune LLM agents with online reinforcement learning
#3Thanks for creating this!
Re: Show HN: LlamaGym – fine-tune LLM agents with online reinforcement learning
#4Interesting project, basically a wrapper too around openai gym-like functionality that can handle open llms.
Yup, it does simplify LLM agent inference on Gym environments but the main technical contribution is reducing your would-be code overhead for online RL
Re: Show HN: LlamaGym – fine-tune LLM agents with online reinforcement learning
#5Twitter thread: https://x.com/khoomeik/status/1766805213644800011?s=46
Re: Show HN: LlamaGym – fine-tune LLM agents with online reinforcement learning
#6Thank you for making this. Simplifying any aspect of RL is always welcome.
Re: Show HN: LlamaGym – fine-tune LLM agents with online reinforcement learning
#7Thank you for making this. Simplifying any aspect of RL is always welcome.
Thanks! Yeah RL for LLMs is pretty underexplored I think beyond the RLHF stuff. Pretty tough to get working tho.
Re: Show HN: LlamaGym – fine-tune LLM agents with online reinforcement learning
#8Can this be used outside of OpenAI environments? If yes I think an example would be great!
Re: Show HN: LlamaGym – fine-tune LLM agents with online reinforcement learning
#9Can this be used outside of OpenAI environments? If yes I think an example would be great!
Gymnasium is now maintained by the Farama Fpundation, an open-source consortium, not OpenAI. But most RL environment work for the past 5+ years has been Gym-compliant. The TextWord example in the repo, for example, instantiates a Gym-style environment but it doesn’t import from Gymnasium (uses textworld.gym instead).
Re: Show HN: LlamaGym – fine-tune LLM agents with online reinforcement learning
#10Very interesting!