Right On Q: Recovering Reasoning under Quantization via Self-Generated Trajectories
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2609.26708v1 Announce Type: new Abstract: Quantization-aware distillation (QAD) restores much of the short-form question-answering performance lost to sub-3-bit quantization, yet leaves mathema...
OnlineQAT introduces a two-stage framework for ultra-low-bit large language models, beginning with block-wise quantization-aware training to create a usable low-bit initialization and then applying on-policy distillation (OPD) using student-generated responses. The method employs a frozen full-precision teacher to provide a sampled reverse-KL training signal at each visited prefix, addressing the mismatch between offline recovery data and the deployed model’s self-generated prefixes. Experiments on Qwen3-1.7B show that OnlineQAT achieves the best average performance among compared quantized methods, outperforming ReasoningQAT by 2.90 and 0.44 points at W3A16 and W2A16 respectively, highlighting the benefit of student-visited states for recovery.
arXiv:2606. 02011v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) rely on long reasoning traces, making inference expensive.
arXiv:2606. 00206v1 Announce Type: new Abstract: Post-training quantization (PTQ) is widely used to deploy large language models efficiently, but its effect on reasoning models is not well understood.
Large Reasoning Models (LRMs) rely on long reasoning traces, making inference expensive. While low-bit quantization reduces per-token decoding cost, we show that aggressive 2-bit inference can fail to deliver end-to-end speedup because instability in the generation process inflates total token count.
arXiv:2512. 18934v2 Announce Type: replace-cross Abstract: Catastrophic forgetting poses a fundamental challenge in continual learning, particularly when models are quantized for deployment efficiency.