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RAMP: Reinforcement Adaptive Mixed Precision Quantization for Efficient On Device LLM Inference paper by Arpit Singh Gautam
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RAMP: Reinforcement Adaptive Mixed Precision Quantization for Efficient On Device LLM Inference

by Arpit Singh Gautam arxiv/2603.17891
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Post training quantization is essential for deploying large language models (LLMs) on resource constrained hardware, yet state of the art methods enforce uniform bit widths across layers, yielding suboptimal accuracy efficiency trade offs. We present RAMP (Reinforcement Adaptive Mixed Precision), an off policy Soft Actor Critic framework that learns per layer bit width assignments to minimize perplexity under a global bit budget. The policy conditions on an 11 dimensional embedding of activat...

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BibTeX 
@misc{arxiv_2603_17891,
  author = {Arpit Singh Gautam},
  title = {RAMP: Reinforcement Adaptive Mixed Precision Quantization for Efficient On Device LLM Inference Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2603.17891}},
  note = {Accessed via Free2AITools.}
}
APA Style
Arpit Singh Gautam. (2026). RAMP: Reinforcement Adaptive Mixed Precision Quantization for Efficient On Device LLM Inference [Paper]. Free2AITools. https://arxiv.org/abs/2603.17891

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Authority (A) 0
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Quality (Q) 60

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FNI V2.0 for RAMP: Reinforcement Adaptive Mixed Precision Quantization for Efficient On Device LLM Inference: Authority (A:0), Popularity (P:0), Recency (R:62), Quality (Q:60). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"Post training quantization is essential for deploying large language models (LLMs) on resource constrained hardware, yet state of the art methods enforce uniform bit widths across layers, yielding suboptimal accuracy efficiency trade offs. We present RAMP (Reinforcement Adaptive Mixed Precision), an off policy Soft Actor Critic framework that learns per layer bit width assignments to minimize perplexity under a global bit budget. The policy conditions on an 11 dimensional embedding of activat..."

❝ Cite Node 

@article{Gautam2026RAMP:,
  title={RAMP: Reinforcement Adaptive Mixed Precision Quantization for Efficient On Device LLM Inference},
  author={Arpit Singh Gautam},
  journal={arXiv preprint arXiv:2603.17891},
  year={2026}
}

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Arpit Singh Gautam

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id
2603.17891
slug
2603.17891
source
arxiv
author
Arpit Singh Gautam
license
arXiv
tags
arxiv:cs.LG, arxiv:cs.AI, llm, reinforcement

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