Newest papers

CheckDistill: Adaptive Checklist Distillation for Efficient Distributional Reward Modeling in Image Editing

Yaqi Cai, Zuan Gao, Mengting Chen, Wei Li, Bo Rui Zheng · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26740 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

インストラクションガイド画像編集のためのチェックベン強化学習は、信頼性、正確性、効率的なフィードバックを提供する報酬モデルを必要とします。直接スケール予測は、多次元編集の好みを圧縮しますが、生成評価者は明確な推論を通じて彼らの判断を暴露することができます。しかし、広範なスコア評価基準は、インスタンス依存の編集と保存制限についてすべての報酬モデルを指定しません、そして長いスケール予測軌道を生成することは、オンライン最適化のための費用がかかります。我々はチェックディスティルを導入し、指示適応チェックリストを内部化するポイント向上の報酬モデリング枠組みです。我々は、各編集条件を

Machine translation (Meta M2M100); the English original follows.

Abstract

Reinforcement learning for instruction-guided image editing requires reward models that provide reliable, accurate, and efficient feedback. Direct scalar prediction compresses multidimensional editing preferences, whereas generative evaluators can expose their judgment through explicit reasoning. However, broad scoring criteria do not specify all instance-dependent editing and preservation constraints, and generating long reasoning trajectories is costly for online optimization. We introduce CheckDistill, a pointwise reward modeling framework that internalizes instruction-adaptive checklists. We decompose each editing condition into atomic, independently verifiable requirements and construct four-dimensional scoring labels together with structured verification evidence. After supervised score initialization, a privileged teacher conditions on checklists, item-level judgments, and dimension-level rationales to supervise the student's own scoring trajectories. A reliability-weighted on-policy self-distillation objective adjusts supervision at score tokens according to agreement between the teacher's expected scores and reference labels, while retaining candidate probability weights in a truncated token support. The deployed student directly predicts four score distributions without checklist inputs or explicit reasoning and aggregates their expectations into a scalar reward. CheckDistill achieves aggregate accuracies of 72.30%, 56.67% and 82.90% on MMRB2, MER-Bench and EditReward-Bench, respectively, exceeding the compared open-source baselines on all three metrics.

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Topics: Computer science, Verifiable secret sharing, Machine learning, Artificial intelligence, Image editing, Security token

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