Few Bits, One Law: Toward W2A4KV2
cs.AI, cs.CL, cs.LG
Submitted: 2026-10-06
Updated: 2026-10-06
Sources
- QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs
- Program Synthesis with Large Language Models
- Quantizing With Randomized Hadamard Transforms: Efficient Heuristic Now Proven
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- Training Verifiers to Solve Math Word Problems
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- BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
- SqueezeLLM: Dense-and-Sparse Quantization
- AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
- SpinQuant: LLM quantization with learned rotations
- ParetoQ: Improving Scaling Laws in Extremely Low-bit LLM Quantization
- The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
- PB-LLM: Partially Binarized Large Language Models
- BitNet: Scaling 1-bit Transformers for Large Language Models
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- Qwen3 Technical Report
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