Apple Machine Learning Research

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   Apple's RISED uses rubrics for multi-environment agent training

   Apple Machine Learning Research published RISED, a method that repurposes rubrics to guide online data selection and policy supervision when training a single LLM agent jointly across diverse interactive environments. An LLM judge tags each rollout with a rubric vocabulary shared across environments; the resulting profiles select data that matches the mixed-environment batch's behavioural composition while limiting overlap, positive rubrics provide privileged context for an on-policy self-distillation teacher's token-level supervision, and negative rubrics steer later rollouts away from recurring failure modes. The paper reports that across model backbones RISED achieves the highest mean pass rate across environments and ranks first or second in every individual environment.

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