From the 1 of 8 linked papers with an AI index.
6 papers · 1 filter
Reinforcement Learning for Code Optimization
Pierre Chambon, Kunhao Zheng, Juliette Decugis +2
The paper proposes a reinforcement‑learning framework that learns to optimize program execution speed by addressing measurement noise, sparse rewards, and instability, using a cali…
DecompRL: Solving Harder Problems by Learning Modular Code Generation
Juliette Decugis, Fabian Gloeckle, Francis Bach +2
How can Large Language Models (LLMs) solve problems they currently cannot? Repeated sampling scales test-time compute but GPU cost grows linearly with attempts, while reinforcement…
Don't Let Gains FADE: Breaking Down Policy Gradient Weights in RL
Juliette Decugis, Sean O'Brien, Francis Bach +2
Reinforcement learning post-training dramatically improves LLM reasoning, but suffers from training instability and diversity collapse. Advantage functions offer an appealing fix:…
Extrapolative Weight Averaging Reveals Correctness-Efficiency Frontiers in Code RL
Kunhao Zheng, Pierre Chambon, Juliette Decugis +4
Linear interpolation between fine-tuned checkpoints has been shown to trace the Pareto front between competing objectives, but whether extrapolative weight averaging can extend suc…
MetaLLM: A High-performant and Cost-efficient Dynamic Framework for Wrapping LLMs
Quang H. Nguyen, Thinh Dao, Duy C. Hoang +4
The rapid progress in machine learning (ML) has brought forth many large language models (LLMs) that excel in various tasks and areas. These LLMs come with different abilities and…
The Extrapolation Power of Implicit Models
Juliette Decugis, Alicia Y. Tsai, Max Emerling +2
In this paper, we investigate the extrapolation capabilities of implicit deep learning models in handling unobserved data, where traditional deep neural networks may falter. Implic…