Home Knowledge Base Adaptive Quantization Noise

Adaptive Quantization Noise

No mentions found

This entity hasn't been tracked yet, or Iris is still building its knowledge base.

Related Articles from SNS

ProjQ: Project-and-Quantize for Adapter-Aware LLM Compression

arXiv:2606.00494v2 Announce Type: replace Abstract: Post-Training Quantization (PTQ) and Low-Rank Adaptation (LoRA) constitute the standard pipeline for efficient Large Language Model (LLM) deployment. However, applying them sequentially poses a problem: PTQ often leaves behind random noise that is spread out (across the model's weights) in a way LoRA can't easily fix, meaning that LoRA ends up wasting its limited capacity trying to fix uncorrectable noise instead of improving task...

arXiv CS 7d ago

Decomposing MXFP4 quantization error for LLM reinforcement learning: reducible bias, recoverable deadzone, and an irreducible floor

arXiv:2605.20402v3 Announce Type: replace Abstract: MXFP4 arithmetic can dramatically accelerate reinforcement learning (RL) post-training of large language models (LLMs), yet the quantization error introduces severe accuracy degradation. Existing work treats the quantization error as a monolithic noise term, missing the distinct mechanisms upon interpreting how quantization error damages training. We prove an exact three-way decomposition of quantization error and show how each component...

arXiv CS 7d ago

Fine-tuning an LLM to write docs like it's 1995

Fine-tuning an LLM to write docs like it's 1995 In my predictions for 2030 I wrote that tech writers would be using specialized LLMs, running locally on powerful hardware. I see hints of this move to “local first” among engineering pundits, but we’re not there yet, in part because of how much more powerful connected frontier models are. That doesn’t mean we can’t experiment, though.

Hacker News 5d ago

Semantic Forwarding and Codebook-Enhanced Model Division Multiple Access for Satellite-Terrestrial Networks

arXiv:2603.02536v2 Announce Type: replace Abstract: Satellite-terrestrial communications are severely constrained by high path loss, limited spectrum resources, and time-varying channel conditions, rendering conventional bit-level transmission schemes inefficient and fragile, particularly in low signal-to-noise ratio (SNR) regimes. Semantic communication has emerged as a promising paradigm to address these challenges by prioritizing task-relevant information over exact bit recovery. In this...

arXiv CS 2d ago

I Put a Datacenter GPU in My Gaming PC for £200

I Put a Datacenter GPU in My Gaming PC for £200 I already had an RTX 4080. Good enough for gaming, not good enough for the models I wanted to run locally. The next step up in GPU land is either spend a fortune on a card with more VRAM, or find another way.

Hacker News 10d ago

Human-Like Neural Nets by Catapulting

Human-like Neural Nets by Catapulting Speculative proposal to create artificial neural nets with human-like performance by high-learning-rate/regularization training of overparameterized NNs to trigger catapulting/grokking. Over-parameterization as a route to true generalization would resolve many outstanding mysteries of artificial versus natural intelligence. There are many mysteries about deep learning and human intelligence, but we could describe the biggest anomaly this way: why are...

Hacker News 3d ago