arXiv study shows coding agents can compress LLMs
Original title: CompressionBench: How can your coding agents compress LLMs?
29 of 36 agent submissions pass verification, achieving 77.33% downstream accuracy on compressed models.
The paper tests whether coding agents can autonomously compress pretrained LLMs while retaining behavior within storage limits. Across six agents and six model-size settings, 29 of 36 submissions pass independent verification. Lowest-perplexity agents yield 3.4% perplexity increase over BF16 and 77.33% downstream accuracy. Agents lower PPL in 21 of 24 revisions at nearly fixed storage; complete-model feedback reverses 17 of 79 prior local rankings. Representation design, experimental judgment, and capability validation are central.
- 29 of 36 agent submissions pass verification
- average 3.4% perplexity increase over BF16
- 77.33% downstream accuracy
- agents lower PPL in 21 of 24 revisions
- complete-model feedback reverses 17 of 79 local rankings
From the original article
1 min read at arxiv.orgRecursive self-improvement requires AI systems to improve the models and infrastructure used by subsequent systems. LLM compression offers a concrete efficiency objective: retain a pretrained model's behavior within a smaller model storage budget. We study whether coding agents can carry out this step autonomously.
By Xinrui Zhong · arxiv.org
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.