OpenForgeRL — An Open Framework for Training Harness-Based Agents in Real Environments
Featured on Hugging Face Daily Papers, **OpenForgeRL** is an open-source framework for end-to-end reinforcement learning of AI agents that rely on "harnesses" (inference scaffolds) such as Claude Code, Codex, and OpenClaw.
Conventional SFT/RL stacks struggled to represent stateful, multi-process harness inference, making it hard to train agents directly in production environments. OpenForgeRL solves this with a combination of a **lightweight proxy that records the harness's model calls** and a **Kubernetes orchestrator that runs each rollout in an isolated remote container**. By decoupling training from inference, it lets researchers train and improve agents directly in the same harness and environment they deploy to.
The evaluation results are worth a look. OpenForgeClaw scores pass^3=31.7 and pass@3=55.9 on ClawEval, while OpenForgeGUI records 37.7 on OSWorld-Verified, 63.0 on Online-Mind2Web, and 72.3 on WebVoyager — beating similarly sized open baselines on nearly every benchmark. In the GUI setting, it rivals models several times larger.
The paper also analyzes how harness choice (ZeroClaw, OpenClaw, Codex, etc.) and RL affect agent behavior. While RL improves self-verification, tool coverage, and multi-step plan completion rates, **critical capabilities like error recovery remain weak**. The next challenge for agent reliability has come into focus.
AMD Instella-MoE-16B-A3B — AMD Formally Enters the Open-Source Model Market
This one was shared on Reddit's r/LocalLLaMA: AMD has released **Instella-MoE-16B-A3B-Think** on Hugging Face. It's a Mixture-of-Experts model with 16B total / 3B active parameters, and it comes with Think (reasoning) capabilities.
According to the person who shared it, they stumbled on it while browsing Hugging Face, so serious community evaluation is only just getting started. AMD is best known as a GPU maker, so the company shipping its own open-weight language model is notable for ecosystem diversity too. With the advantage of controlling both hardware and model optimization in-house, it will be interesting to see how far that strength carries — future benchmark results should tell.
VideoChat3 — A Fully Open MLLM That Makes Video Understanding Efficient at 4B Parameters
A paper listed on Papers with Code (the PwC title is "DataFlow," but the paper itself introduces **VideoChat3**) presents a video-centric multimodal LLM with **4B parameters**.
VideoChat3 covers **three video understanding scenarios** — general video, long-form video, and streaming video — in a single model. Efficiency comes from two designs: "I3D-ViT (Inflated 3D Vision Transformer)" and "Adaptive Frame Resolution for Streaming Video Perception." Together they cut the cost of processing video input during both training and inference.
On the effectiveness side, the team built three diverse datasets — VideoChat3-Academic2M, VideoChat3-LV116K, and VideoChat3-OL617K — improving generalization across the general, long-form, and streaming scenarios.
Most notable is the **fully open** policy: training code, strategies, and datasets are all provided. The groundwork for reproducibility and community-driven improvement is in place. Despite being just 4B, it outperforms existing open models of similar or larger scale across multiple benchmarks, making it a strong candidate for edge-oriented video understanding.
(Bonus) TechCrunch Reviews OpenAI's AI Keypad
TechCrunch has published a hands-on report on the **AI keypad** announced by OpenAI. Their take: it could be a fun tool for some coders, but it's a product far removed from most people — it looks set to settle in as a niche gadget for developers.
Takeaways
What the three announcements share is a landing point close to real-world operation. OpenForgeRL means agents can be trained in the actual harness environments they run in; AMD Instella-MoE opens up the possibility of hardware-vendor-driven, optimized open models; and VideoChat3 secures reproducibility through its fully open policy. It's fair to say open-source AI has taken another step into practical territory.
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