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Omni-modal foundation models achieve self-improvement via RL compute scaled to 1M context, leveraging asynchronous training and agentic grading for enhanced intelligence.
Web agents can now achieve state-of-the-art performance and robust test-time scaling by learning to self-verify their actions, bypassing the need for costly external judges during deployment.
Hybrid RL for bus holding suffers when simulators misrepresent passenger journey completion, leading to worse outcomes than purely offline methods.
The single mid-run human intervention that pulled the community out of a monoculture is described, what the trace does and does not establish, and the controlled comparison that would settle whether shared research state improves discovery per unit of compute is described.
Molt reduces the cognitive load on researchers by offering a clean and compact codebase that maintains high performance, enabling faster iterations in agentic reinforcement learning.
