MetaEvo is a two-stage framework using preference optimization for principle abstraction followed by modular reuse to enable continual improvement of LLM agents on reasoning tasks.
Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulation , booktitle =
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
MetaEvo: A Meta-Optimization Framework for Experience-Driven Agent Evolution
MetaEvo is a two-stage framework using preference optimization for principle abstraction followed by modular reuse to enable continual improvement of LLM agents on reasoning tasks.