Chain-of-Thought (CoT) reasoning has become a key problem-solving capability for advanced large language models (LLMs), but the "over-thinking" issue still plagues LLMs. In this paper, we propose a novel learning scheme termed Adaptive-Budgeting based Policy Optimization (ABPO) to balance the performance and efficiency of CoT reasoning. Our ABPO defines the RL training as an adaptive curriculum learning process, where example pools are curated to categorize training examples into three types, namely the mastered, learning and hard ones, respectively. As the training progresses, ABPO will adaptively schedule the examples with proper length budgets, and the example pools will also be dynamically updated based on the model status, thereby achieving a good balance between efficiency and performance of CoTs. The experimental results not only show the substantial efficiency improvements by ABPO, e.g., reducing token length by 78.3% while improving 2.0% performance of DeepSeek-R1-Distill-Qwen-1.5B on average, but also show its obvious advantages over the compared methods, e.g., reducing 59.4% length and increasing 8.3% performance on average than HAPO, respectively. Our code is released at https://github.com/SenriAkane1/ABPO/

Given the long textual product information and the product image, Multi-modal Product Summarization (MPS) aims to increase customers’ desire to purchase by highlighting product characteristics with a short textual summary. Existing MPS methods can produce promising results. Nevertheless, they still 1) lack end-to-end product summarization, 2) lack multi-grained multi-modal modeling, and 3) lack multi-modal attribute modeling. To improve MPS, we propose an end-to-end multi-grained multi-modal attribute-aware product summarization method (MMAPS) for generating high-quality product summaries in e-commerce. MMAPS jointly models product attributes and generates product summaries. We design several multi-grained multi-modal tasks to better guide the multi-modal learning of MMAPS. Furthermore, we model product attributes based on both text and image modalities so that multi-modal product characteristics can be manifested in the generated summaries. Extensive experiments on a real large-scale Chinese e-commence dataset demonstrate that our model outperforms state-of-the-art product summarization methods w.r.t. several summarization metrics. Our code is publicly available at: https://github.com/KDEGroup/MMAPS.