31/07/2026
🚀 𝐏𝐢 𝐀𝐈 𝐖𝐞𝐞𝐤𝐥𝐲 𝐓𝐫𝐞𝐧𝐝𝐬 𝟗𝟓 𝐢𝐬 𝐡𝐞𝐫𝐞!
It’s Friday! Get ready to stay ahead with the latest AI breakthroughs, handpicked by our Deep Learning Scientist, Jino Rohit.
This week’s highlights:
💻 𝐎𝐩𝐞𝐧𝐅𝐨𝐫𝐠𝐞 𝐑𝐋: 𝐓𝐫𝐚𝐢𝐧 𝐇𝐚𝐫𝐧𝐞𝐬𝐬 𝐍𝐚𝐭𝐢𝐯𝐞 𝐀𝐠𝐞𝐧𝐭𝐬 𝐢𝐧 𝐚𝐧𝐲 𝐄𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭
OpenForgeRL is an open-source framework for training AI agents directly inside the same production inference harnesses they use at deployment (e.g., Claude Code, Codex, OpenClaw), eliminating the train–deploy mismatch. It decouples training and inference using a lightweight proxy and Kubernetes-based remote rollouts, making 𝐚𝐧𝐲 𝐡𝐚𝐫𝐧𝐞𝐬𝐬 𝐚𝐧𝐝 𝐚𝐧𝐲 𝐞𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭 compatible with standard RL frameworks like veRL. With only hundreds to a few thousand RL tasks, it outperforms similarly sized open models across tool-use and GUI benchmarks, while showing that RL significantly improves self-verification, tool usage, and multi-step planning—though error recovery remains a key challenge.
🌐 https://pischool.link/6431af
🗣️ 𝐒𝐤𝐢𝐥𝐥 𝐒𝐞𝐥𝐟-𝐏𝐥𝐚𝐲: 𝐏𝐮𝐬𝐡𝐢𝐧𝐠 𝐭𝐡𝐞 𝐅𝐫𝐨𝐧𝐭𝐢𝐞𝐫 𝐨𝐟 𝐋𝐋𝐌 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐰𝐢𝐭𝐡 𝐂𝐨-𝐄𝐯𝐨𝐥𝐯𝐢𝐧𝐠 𝐒𝐤𝐢𝐥𝐥𝐬
Skill Self-Play (Skill-SP) is a reinforcement learning framework that enables LLMs to 𝐜𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬𝐥𝐲 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐬𝐞𝐥𝐟-𝐩𝐥𝐚𝐲 by co-evolving three components: a task proposer, a solver, and a dynamic skill controller. Instead of relying solely on environment feedback or unconstrained self-generated tasks, it maintains a growing library of 𝐯𝐞𝐫𝐢𝐟𝐢𝐚𝐛𝐥𝐞 𝐚𝐠𝐞𝐧𝐭 𝐬𝐤𝐢𝐥𝐥𝐬that balance reliable supervision with open-ended exploration. Across reasoning and tool-use benchmarks, Skill-SP consistently boosts capable models while dramatically improving initially misaligned ones, demonstrating a scalable path toward autonomous capability growth without manual data annotation.
🌐 https://pischool.link/f305ab
🧠 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐍𝐚𝐭𝐢𝐯𝐞 𝐌𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐏𝐫𝐞-𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐅𝐫𝐨𝐦 𝐒𝐜𝐫𝐚𝐭𝐜𝐡
This paper provides the first scaling laws for native multimodal pre-training, studying how to optimally allocate model size, training tokens, and multimodal data under a fixed compute budget. The authors show that compute-optimal configurations follow predictable power laws, with text-heavy datasets becoming more efficient only at larger model scales, and derive an efficiency frontier for choosing the best model/data mix. They also demonstrate that native multimodal pre-training improves cross-modal transfer, boosting text-only spatial reasoning and enabling strong multimodal in-context learning.
🌐 https://pischool.link/ad1fb5
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