Users praise the curated papers on world modeling for agentic reinforcement learning as a useful deep dive and key step toward more reliable, generalizable agents.
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@cwolferesearch This is a very interesting direction! Excited to read your post - you might like this post as a through line for why these ideas work https://substack.com/@drfeifei/note/p-200386248?r=b120e&utm_medium=ios&utm_source=notes-share-action
@micmylin thanks for sharing!
@OKfallah thank you for sharing!
@cwolferesearch Looking forward to it!
Working on a blog that covers recent research on agents + world modeling. One of those rare topics that is both (relatively) easy to understand and practically useful for anyone working on agentic RL. So far, I have included the following papers and blogs: - ECHO: https://arxiv.org/abs/2605.24517 - Prime Intellect empirical ECHO analysis: https://www.primeintellect.ai/blog/true-agents-model-the-world - PaW: https://arxiv.org/abs/2606.02388 - Qwen-AgentWorld: https://arxiv.org/abs/2606.24597 What am I missing? Are there any other good papers that explore incorporating world modeling into the training process for agents?
@cwolferesearch This is a very interesting direction! Excited to read your post - you might like this post as a through line for why these ideas work https://substack.com/@drfeifei/note/p-200386248?r=b120e&utm_medium=ios&utm_source=notes-share-action
Working on a blog that covers recent research on agents + world modeling. One of those rare topics that is both (relatively) easy to understand and practically useful for anyone working on agentic RL. So far, I have included the following papers and blogs: - ECHO: https://arxiv.org/abs/2605.24517 - Prime Intellect empirical ECHO analysis: https://www.primeintellect.ai/blog/true-agents-model-the-world - PaW: https://arxiv.org/abs/2606.02388 - Qwen-AgentWorld: https://arxiv.org/abs/2606.24597 What am I missing? Are there any other good papers that explore incorporating world modeling into the training process for agents?
Users praise the curated papers on world modeling for agentic reinforcement learning as a useful deep dive and key step toward more reliable, generalizable agents.
Based on 11 visible X reactions from 11 accounts; directional sample.
Ask a question below.
Published answers will appear here.