Core Insights - Test-Time Scaling has become a key pathway to enhance model inference capabilities, with Block Diffusion Language Models (BDLMs) emerging as strong competitors to traditional autoregressive models due to their unique parallel decoding abilities [2] - Existing BDLMs face a dilemma in efficiency versus effectiveness during long-chain reasoning, where large block decoding is fast but prone to errors, while small block decoding is accurate but loses parallel advantages [2][12] - A new framework called TDAR has been proposed by Fudan University and Peking University, which introduces the "Think Coarse, Critic Fine" (TCCF) paradigm and Bounded Adaptive Confidence Decoding (BACD) to break the trade-off between speed and accuracy [2][6] Summary by Sections TDAR Framework - The TDAR framework includes two core designs: Bounded Adaptive Confidence Decoding (BACD) and the TCCF paradigm, aimed at addressing the efficiency and accuracy challenges in long-chain reasoning [6][11] Bounded Adaptive Confidence Decoding (BACD) - BACD dynamically adjusts the denoising threshold based on the average confidence of generated tokens, incorporating upper and lower bounds to balance aggressive acceleration and conservative measures during uncertain steps [9][20] TCCF Paradigm - The TCCF paradigm differentiates between exploration and verification phases in long-chain reasoning, allowing for coarse thinking during exploration and fine verification during validation, thus optimizing computational granularity [11][15] Experimental Results - TDAR-8B-Thinking achieved superior performance across six mainstream reasoning benchmarks, surpassing the previous state-of-the-art model TraDo-8B by 3.4 percentage points, with decoding speed increasing from 1.27 TPF to 2.97 TPF [13][16] - With the integration of BACD, speed further improved to 3.37 TPF, and accuracy increased by 1.6 percentage points; the TCCF paradigm led to a significant accuracy boost from 36.3% to 42.9% on complex tasks while maintaining a high speed of 3.04 TPF [13][16] Performance Analysis - The research team conducted a multi-dimensional analysis of the performance sources of TDAR, focusing on the impact of block size, decoding strategies, and the TCCF paradigm [17][18] - BACD demonstrated superior stability compared to traditional decoding methods, effectively avoiding issues like model collapse and repeated generation [19][20] - The analysis identified a sweet spot for block size at 16 for the 8B model, balancing speed and quality through progressive training [23][26] Conclusion and Future Outlook - The introduction of TDAR marks a significant advancement for BDLMs in complex reasoning tasks, allowing for large block sizes to maintain quality and speed [31][32] - TDAR provides an efficient solution for Test-Time Scaling in BDLMs and offers new insights for the design of future parallel reasoning models [32]