Year: 2026
Type: conference-paper
Abstract: Learning from preference-based feedback has become an effective approach for aligning LLMs across diverse tasks.However, highquality human-annotated preference data remains expensive and scarce.Existing methods address this challenge through either selfrewarding, which scales by using purely AIgenerated labels but risks unreliability, or active learning, which ensures quality through oracle annotation but cannot fully leverage unlabeled data.In this paper, we present COACT, a novel framework that synergistically combines selfrewarding and active learning through strategic human-AI collaboration.COACT leverages self-consistency to identify both reliable selflabeled data and samples that are requiring oracle verification.Additionally, oracle feedback guides the model to generate new instructions within its solvable capability.Evaluated on three reasoning benchmarks across two model families, COACT achieves average improvements of +13.25% on GSM8K, +8.19% on MATH, and +13.16% on WebInstruct, consistently outperforming all baselines.1
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166806993