AdaptEvo: Adaptive Agent Learning with Evolving Supervision

Key points

• Rule-governed contextual decision tasks require models to apply specified rules to case-specific context and evidence.1
• Its Evolution module synthesizes reusable decision knowledge from recurring failures across training cases and refines process rubrics to detect overlooked errors.1
• To support empirical evaluation, we construct an industrial multimodal content moderation dataset comprising a training set and In-Period and Out-of-Period test sets, with the latter collected under changed rules.1
• On Out-of-Period, the policy trained with CA-GRPO retains exact-label accuracy gains over the base model across evaluated checkpoints without injected decision knowledge, while GRPO declines with continued training.1

Sources (1)

• [1]AdaptEvo: Adaptive Agent Learning with Evolving Supervision
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 06:47 AM

  To address these challenges, we introduce AdaptEvo, a framework for learning under imperfect supervision that couples confidence-adaptive policy optimization with evolving decision knowledge and evaluation rubrics.

  Rule-governed contextual decision tasks require models to apply specified rules to case-specific context and evidence.

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