Large reasoning models (LRMs) have attracted much attention due to their exceptional performance. However, their performance mainly stems from thinking, a long Chain of Thought (CoT), which significantly increase computational overhead. To address this overthinking problem, existing work focuses on using reinforcement learning (RL) to train hybrid reasoning models that automatically decide whether to engage in thinking or not based on the complexity of the query. Unfortunately, using RL will suffer the the reward hacking problem, e.g., the model engages in thinking but is judged as not doing so, resulting in incorrect rewards.To mitigate this problem, existing works either employ supervised fine-tuning (SFT), which incurs high computational costs, or enforce uniform token limits on non-thinking responses, which yields limited mitigation of the problem.In this paper, we propose Thinking-Based Non-Thinking (TNT). It does not employ SFT, and sets different maximum token usage for responses not using thinking across various queries by leveraging information from the solution component of the responses using thinking. Experiments on five mathematical benchmarks demonstrate that TNT reduces token usage by around 50\\%$ compared to DeepSeek-R1-Distill-Qwen-1.5B/7B and DeepScaleR-1.5B, while significantly improving accuracy. In fact, TNT achieves the optimal trade-off between accuracy and efficiency among all tested methods. Additionally, the probability of reward hacking problem in TNT’s responses, which are classified as not using thinking, remains below $10\\%$ across all tested datasets.

The application of visual-to-music generation (VTM) is rapidly growing. However, current VTM methods struggle with capturing the relationship between visuals and music in open-domain settings, mainly due to two challenges: the lack of large-scale, high-quality visual-music paired datasets and the absence of direct semantic correspondence between visuals and music. In this work, we propose CoT-VTM, a framework that distills Chain-of-Thought (CoT) reasoning to enable visual-to-music generation without paired data, while efficiently producing music aligned with visual content in open-domain settings. We first bridge the gap between visual, music, and text data using appropriate foundation models. Next, we identify key elements of the visual-music relationship and design a CoT prompt for visual-to-music mapping. To fully distill the reasoning of CoT, we incorporate latent information from intermediate reasoning steps as supervisory signals alongside visual and music supervision. Finally, we design a two-stage mapping distillation training process: the first stage uses discriminative MLP modules, while the second uses a generative embedding diffusion model (EDM). Our model achieves optimal performance on both image-to-music and video-to-music tasks. Project page: https://xxkkxxx.github.io/cot-vtm/