Attend to Your Own Thoughts: Breaking the Barrier for Post-Training Quantization of Reasoning LLMs through the Lens of 1.58-Bit Quantization

arXiv:2608.01078 · cs.CL, cs.AI · Submitted 2026-08-02 · Read on arXiv

Shigeng Wang, Chao Li, Yangyuxuan Kang, Jiawei Fan, Anbang Yao

cs.CL, cs.AI

Submitted: 2026-08-02

Comments: This research work was completed and submitted for publication in early May 2026. The project page: https://github.com/IntelChina-AI/BitTern

Code: https://github.com/IntelChina-AI/BitTern

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Calibration — 137× in this paper · explained in 7 episodes
• Quantization — 79× in this paper · explained in 12 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Trace — 11× in this paper · explained in 2 episodes
• Adam — 10× in this paper · explained in 2 episodes
• Benchmark — 10× in this paper · explained in 2 episodes
• Generalization — 9× in this paper · explained in 13 episodes
• Reasoning Traces — 9× in this paper · explained in 3 episodes
• Attend to Your Own Thoughts — 7× in this paper
• Pruning — 7× in this paper · explained in 4 episodes
• Core — 6× in this paper · explained in 2 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Distillation — 3× in this paper · explained in 2 episodes
• Elias — 3× in this paper · explained in 2 episodes
• Perplexity — 3× in this paper · explained in 3 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Downstream Tasks — 2× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Chain-of-Thought (CoT) — 1× in this paper · explained in 4 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• KV cache — 1× in this paper · explained in 3 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Mixture-of-Experts (MoE) — 1× in this paper · explained in 7 episodes
• Mixture-of-Experts (MoE) Models — 1× in this paper · explained in 3 episodes
• Mostly Basic Programming Problems — 1× in this paper
• Quantization-Aware Training — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• OpenAI o1 System Card
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• GPT-4 Technical Report
• Qwen3 Technical Report
• OpenAI GPT-5 System Card
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
• GPT-4o System Card
• Kimi k1.5: Scaling Reinforcement Learning with LLMs
• Qwen3.5-Omni Technical Report
• Ternary Weight Networks
• Distilling the Knowledge in a Neural Network
• BitNet Distillation
• TernaryLLM: Ternarized Large Language Model
• The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
• BitNet v2: Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs
• Tequila: Trapping-free Ternary Quantization for Large Language Models
• LLaMA: Open and Efficient Foundation Language Models
• Llama 2: Open Foundation and Fine-Tuned Chat Models
• BitNet b1.58 2B4T Technical Report
• OpenCodeInstruct: A Large-scale Instruction Tuning Dataset for Code LLMs

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