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/

Large language models (LLMs) are increasingly deployed in high-stakes domains reliant on tabular data (e.g., financial reporting), where undetected logical inconsistencies such as mismatched totals and components can lead to critical errors. Yet, the ability of LLMs to identify such inconsistencies remains poorly understood, hindered by the absence of standardized evaluation frameworks and cell-level annotated datasets. To bridge this gap, we propose a comprehensive benchmark SEC-Fintables comprising 103,395 real-world and error-injected table instances, alongside a novel evaluation protocol that decomposes inconsistency detection into granular sub-tasks. Through evaluating both proprietary and open-source LLMs on SEC-Fintables, we find that contemporary LLMs exhibit only partial competence in detecting logical inconsistencies. Our study reveals key limitations and improvement opportunities for LLMs. We believe SEC-Fintables and our evaluation protocol can serve as a practical resource for advancing reliable inconsistency detection of LLMs in tabular reasoning. We release SEC-Fintables at https://github.com/XIEFOX/SEC-Fintables.