LFQ: Logit-aware Final-block Quantization for Boosting the Generation Quality of Low-Bit Quantized LLMs

Researchers introduce Logit-aware Final-block Quantization (LFQ), a technique that improves low-bit quantization of large language models by optimizing the final transformer block to preserve token probability distributions. This advancement addresses quality degradation in generative tasks while maintaining efficiency gains critical for deploying scaled LLMs.

LFQ tackles a fundamental challenge in making large language models practical for deployment: reducing memory footprint through low-bit quantization without sacrificing generation quality. Current block-wise quantization methods excel at language understanding tasks but falter on complex generation—particularly in extended reasoning chains and longer responses. The researchers identified two root causes: omitting the unembedding layer (LM head) from optimization and relying on mean squared error as the objective function, both of which distort the probability distributions of predicted tokens.

This problem stems from the broader tension between model compression and task performance. As organizations deploy increasingly large models, quantization has become essential infrastructure. However, previous approaches optimized for matching intermediate representations rather than final prediction quality. LFQ's innovation is elegantly simple: instead of minimizing reconstruction error across the model, it focuses final-block optimization on cross-entropy between full-precision and quantized logits, directly preserving the probability distributions users observe.

The implications are significant for practitioners deploying LLMs in production environments. Improved generation quality at lower bit-widths directly translates to cost savings without quality trade-offs—critical for inference-heavy applications like chatbots and reasoning systems. The technique maintains parity with full-precision baselines on standard benchmarks while boosting performance on complex generative tasks, addressing the most visible failure mode users encounter.

Future developments should explore whether logit-aware quantization extends to earlier layers or other model architectures beyond transformers. Integration into popular quantization frameworks could accelerate adoption, making efficient deployment of reasoning-heavy models more accessible to organizations with constrained computational budgets.

• →LFQ improves low-bit quantization by optimizing the final transformer block using cross-entropy loss rather than MSE, aligning token probability distributions with full-precision models.
• →The technique addresses failure modes in complex generation tasks and extended reasoning chains while maintaining accuracy parity on language understanding benchmarks.
• →Omitting the LM head from quantization and using MSE objectives were identified as key causes of generation quality degradation in previous block-wise PTQ methods.
• →LFQ enables memory-efficient deployment of large language models without sacrificing generation quality, reducing inference costs for production systems.
• →The approach is model-agnostic and consistently improves performance across diverse model families without requiring architectural changes.