Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Tasks
Evolutionary fine-tuning enables large language models to develop cross-task problem-solving capabilities by learning from search trajectories, demonstrating improved performance o…
Hugging Face · Daily Papers
·Young-Jun Lee, Seungone Kim
·
·▲ 23 upvotes
Este artigo está em destaque na seleção diária de papers do Hugging Face, curada pela comunidade de pesquisa em IA.
Autores: Young-Jun Lee, Seungone Kim, Minki Kang, Alistair Cheong Liang Chuen, Zerui Chen, Seungho Han
- 23 upvotes da comunidade
- Temas: evolutionary search, large language models, optimization tasks, mathematical conjectures, evolutionary fine-tuning, search trajectories
Resumo
Resumo original (em inglês), extraído do paper:
Evolutionary fine-tuning enables large language models to develop cross-task problem-solving capabilities by learning from search trajectories, demonstrating improved performance on mathematical conjectures and optimization tasks.Onde ler
// relacionados
Leia também
Blog
Using Lift to Turn Research PDFs into Structured JSON with Controlled, Schema-Guided Field-Level Evaluation
Blog
Anthropic Redeploys Claude Fable 5 on July 1 After US Export Controls Lift, Adds New Cybersecurity Classifier
Blog
The latest AI news we announced in June 2026
Blog