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When quantized reasoning models run at 4-bit precision, adaptive computation allocation often terminates prematurely due to inaccurate confidence calibration. BitCal-TTS achieves an accuracy improvement of 3.7% (7B) and 2.8% (14B) on GSM8K through bit-conditional recalibration and inference stability proxy, while reducing the premature termination rate.
Section 01
BitCal-TTS addresses the premature termination problem caused by inaccurate confidence calibration when quantized reasoning models run at 4-bit precision. Through mechanisms like bit-conditional recalibration and inference stability proxy, it achieves an accuracy improvement of 3.7% for the 7B model and 2.8% for the 14B model on GSM8K, while reducing the premature termination rate.
Section 02
Large Reasoning Models (LRMs) exhibit strong performance through chain-of-thought, but their inference process consumes significant resources. Post-training quantization (e.g., 4-bit) can reduce memory and computational overhead, but it leads to inaccurate confidence calibration, causing premature termination (false confidence signals end inference) and over-generation (extending the chain even after obtaining the correct answer). Premature termination is more harmful in resource-constrained scenarios.
Section 03
BitCal-TTS is a lightweight runtime controller with three mechanisms: 1. Online uncertainty proxy (token-level logits distribution analysis + inference trajectory stability observation); 2. Bit-conditional confidence recalibration (increasing the termination threshold under low precision); 3. Bit-aware post-token confirmation window (extending the window to verify answers under low precision).
Section 04
Tested on the GSM8K benchmark using Qwen2.5 7B/14B models (4-bit greedy decoding): The 7B model achieved a 3.7% accuracy improvement, with the premature termination rate dropping from 14.8% to 11.1%; the 14B model achieved a 2.8% improvement, with the termination rate dropping from 17.1% to 11.4%; token efficiency was maintained. The experiment ensured rigor through partial sharding (resource constraints), Wilson confidence interval, and open-source code.
Section 05
BitCal-TTS has advantages such as plug-and-play (no model modification required), minimal computational overhead (implemented via forward hooks), and strong generality (transferable to structured reasoning tasks). It is of significant value in latency-sensitive scenarios like edge computing and real-time customer service.
Section 06
Limitations: Targets greedy decoding scenarios, and the confirmation window is adapted to the GSM8K format. Future directions: Combine advanced quantization technologies like GPTQ/AWQ, and explore dynamic bit-width adjustment scenarios.
Section 07
BitCal-TTS solves the confidence calibration problem of quantized models through concise mechanisms, improving accuracy and reducing the premature termination rate. It reminds us that model compression needs to pay attention to the impact of quantization on metacognitive abilities, and it is an excellent practice in model compression adaptation.
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