19 papers
Left properness of Moore flows
Philippe Gaucher
We introduce the notion of a reparametrization category with cuts. For every such reparametrization category , we prove the tensor lemma, namely that the tensor product…
Semantic Uncertainty-Guided Orchestration in Hierarchical Multi-Agent Systems
John Knowlton, Aritra Guha, Risto Miikkulainen
As large language model (LLM)-based multi-agent systems become increasingly capable, coordinating agents under uncertainty becomes a fundamental challenge. Existing orchestration s…
Evolution Strategies at Scale: LLM Fine-Tuning Beyond Reinforcement Learning
Xin Qiu, Yulu Gan, Conor F. Hayes +6
The paper shows that evolution strategies can successfully fine‑tune billion‑parameter large language models without backpropagation, outperforming reinforcement learning in stabil…
Quantized Evolution Strategies: High-precision Fine-tuning of Quantized LLMs at Low-precision Cost
Yinggan Xu, Kajetan Schweighofer, Risto Miikkulainen +1
Post-Training Quantization (PTQ) is essential for deploying Large Language Models (LLMs) on memory-constrained devices, yet it renders models static and difficult to fine-tune. Sta…
Overcoming Forgetting in LLM Fine-Tuning with Evolution Strategies
Kajetan Schweighofer, Conor F. Hayes, Roberto Dailey +2
Evolution Strategies (ES) has recently emerged as a competitive alternative to reinforcement learning (RL) for large language model (LLM) fine-tuning, offering advantages through s…
Efficient Pre-Training of LLMs through Truncated SVD Layers
Kaivan Kamali, Kajetan Schweighofer, Hormoz Shahrzad +3
The massive scaling of Large Language Models (LLMs) has made pretraining increasingly cost-prohibitive. While low-rank representation and orthonormal weight matrices could in princ…