#meta‑learning
10 papers · 1 filter
Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering
Junlin Yang, Che Jiang, Yu Fu +21
The paper presents Frontis-MA1, a 35‑billion‑parameter model trained as a meta‑evolution agent for machine learning engineering, using a new OpenMLE stack that combines operator le…
One Run Is Not an Idea: The Implementation Lottery in Automated Research
Jingjie Ning, Shanshan Zhong, Xiaochuan Li +2
The paper studies how automated research systems can draw misleading conclusions when they rely on a single implementation of an idea, introducing the concept of an "implementation…
Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations
Seunghun Yu, Meiyi Zhu, Petar Popovski +2
The paper proposes weighted conformal methods for changepoint localization and root‑cause analysis that downweight potentially corrupted observations using uncertainty estimates, a…
Meta-Learned Reward Shaping for Reinforcement Learning from Human Feedback
Yunpeng Chu
The paper proposes MeRLa, a meta‑learning framework that learns task‑specific reward shaping functions to improve reinforcement learning from human feedback for large language mode…
Learning to Learn-at-Test-Time: Language Agents with Learnable Adaptation Policies
Zhanzhi Lou, Hui Chen, Yibo Li +2
The paper introduces Meta-TTL, a bi‑level optimization framework that learns adaptation policies for test‑time learning in language agents, using evolutionary search to improve per…
Meta-Learning Preferences for Multilingual LLM Alignment
Jiaying Lin, Seongho Son, Nam Phuong Tran +3
The paper introduces a meta-learning method that uses preference data from high-resource languages to quickly adapt large language models to low-resource languages with very few hu…