Remote AI Paper Discussion Group

Link to article: https://arxiv.org/abs/2603.10165v2
Title: OpenClaw-RL: Train Any Agent Simply by Talking
Content: OpenClaw-RL is a framework for online RL that lets personal agents improve from the “next-state signals” naturally produced during use, such as user replies, tool outputs, terminal changes, GUI changes, corrections, and feedback. It uses a server–client architecture where deployed agents stream interaction data back to an RL server, while a separate asynchronous server extracts evaluative and directive training signals without slowing inference. Methodologically, it combines broad evaluative feedback with richer but sparser token-level directive supervision, using overlap-guided hint selection and log-probability clipping to stabilize learning across terminal, GUI, software-engineering, and tool-call environments.
Slack link: ml-ka.slack.com, channel: #pdg. Please join us -- if you cannot join, please message us here or to mlpaperdiscussiongroupka@gmail.com.

In the Paper Discussion Group (PDG) we discuss recent and fundamental papers in the area of machine learning on a weekly basis. If you are interested, please read the paper beforehand and join us for the discussion. If you have not fully understood the paper, you can still participate – everyone is welcome! You can join the discussion or simply listen in. The discussion is in German or English depending on the participants.

Link to article: https://openaccess.thecvf.com/content/ICCV2025/papers/Petrov_TriDi_Trilateral_Diffusion_of_3D_Humans_Objects_and_Interactions_ICCV_2025_paper.pdf
Title: TriDi: Trilateral Diffusion of 3D Humans, Objects, and Interactions
Content: TriDi is a unified 3D human-object interaction model that can generate or infer humans, objects, and their interactions in any direction, rather than only conditioning one modality on another. It uses a three-way transformer-based diffusion process and supports user control through either text descriptions or contact maps embedded in a shared latent space. With one network, TriDi covers seven conditional distributions, outperforms specialized one-way baselines on GRAB and BEHAVE, improves diversity, and generalizes to applications like scene population and unseen object geometries.
Slack link: ml-ka.slack.com, channel: #pdg. Please join us -- if you cannot join, please message us here or to mlpaperdiscussiongroupka@gmail.com.

In the Paper Discussion Group (PDG) we discuss recent and fundamental papers in the area of machine learning on a weekly basis. If you are interested, please read the paper beforehand and join us for the discussion. If you have not fully understood the paper, you can still participate – everyone is welcome! You can join the discussion or simply listen in. The discussion is in German or English depending on the participants.