Topic
On Policy Learning
2 episodes
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Beacon: Knowing When and How toPerform Agentic Visual Reasoning
Beacon is a new agentic visual reasoning model from Peking University and the Kling Team that tackles a surprisingly overlooked problem: existing models call visual tools indiscriminately, and the errors they introduce on easy problems cancel out most of the gains they get on hard ones. Beacon fixes this with two RL mechanisms — a necessity-aware adaptive reward and a hint-guided rollout strategy — that teach the model when tools are actually needed and how to use them effectively when they are. Onyx and Echo dig into why the 'just call the tool' default is broken, how the reward design works at the trajectory level, and what this means for anyone building multimodal pipelines today.
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Seed: Self Evolving On Policy Distillation for Agentic Reinforcement Learning
Seed tackles the credit-assignment problem in long-horizon agent reinforcement learning by turning completed trajectories into evolving natural-language hindsight skills, then distilling their effect into dense token-level training signals. Vince sees a potentially shippable training pattern for teams already running agentic RL; Ava likes the on-policy design but wants stronger evidence that self-generated skills do not amplify the model’s own blind spots.