Reliable deployment of large language models (LLMs) requires accurate uncertainty estimation. Existing answer-first methods produce confidence only after generating an answer, thereby measuring the correctness of a specific response and limiting their practical usability. We study a confidence-first paradigm, where the model outputs its confidence before answering, interpreting this score as the model’s probability of answering the question correctly under its current policy.

We propose CoCA(Co-optimized Confidence and Answers), a GRPO reinforcement learning framework that jointly optimizes confidence calibration and answer accuracy via segmented credit assignment. By assigning separate rewards and group-relative advantages to confidence and answer segments, CoCA enables stable joint optimization and substantially mitigates the observed reward-hacking failure mode. Experiments across math, code, and factual QA benchmarks show improved calibration and uncertainty discrimination while preserving answer quality, thereby enabling a broader range of downstream applications.

Multimodal content generation has become an area of considerable interest. However, existing methods are hindered by limitations related to model constraints and training strategies: (1) Most current approaches rely on training models from scratch, resulting in inefficient training processes when extending these models; (2) There is a lack of constraints on adjacent steps within the models, leading to slow sampling and poor generation stability across various sampling methods. To address the issues, we introduce Multimodal Generation with Consistency Transferring (MGCT). The method introduces two key improvements: (1) A Model Consistency Transferring (MCT) strategy to acquire low-cost prior knowledge, increasing training efficiency and avoiding error accumulation; (2) A Layer Consistency Transferring (LCT) between adjacent steps, enhancing denoising capabilities at each step and improving model stability across various generation methods. These strategies ensure the consistency of jointly generated multimodal content and improving training efficiency. Experiments show that the algorithm enhances the model’s ability to capture actions and depict backgrounds more effectively. In both the AIST++ and Landscape datasets, it improves video generation speed by approximately 40% and quality by about 39.3%, while also achieving a slight 3% improvement in audio quality over the baseline.