Papers with full-precision baseline
PE-QAT: Parameter-Efficient Quantization-Aware Training for Large Language Models (2026.acl-srw)
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| Challenge: | Quantization Aware Training (QAT) is expensive to train and unscalable to large models. |
| Approach: | They propose a parameter-efficient framework targeting per-channel 4-bit weight-activation quantization of large language models. |
| Outcome: | The proposed framework preserves accuracy within 0.11 percentage points of the full-precision baseline on Llama-2-7B zero-shot tasks while training only 1.26% of total parameters. |
MixKVQ: Query-Aware Mixed-Precision KV Cache Quantization for Long-Context Reasoning (2026.acl-long)
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| Challenge: | Existing low-bit quantization methods often exhibit severe performance degradation on complex reasoning tasks. |
| Approach: | They propose a plug-and-play method that uses a key channel's intrinsic quantization difficulty and relevance to the query to identify and preserve critical key channels that need higher precision. |
| Outcome: | Experiments on complex reasoning datasets show that the proposed method outperforms low-bit methods at a substantially reduced memory footprint. |