1 citations · 2 across the 5 of their papers we have counts for
5 papers
Le Critique: Privileged Value Functions for LLM Reinforcement Learning
Siddarth Venkatraman, Matthieu Dinot, Laurence Aitchison
Reinforcement learning algorithms for Large Language Models (LLMs) are largely distinguished by their variance reduction strategy. Group-relative methods like GRPO reduce gradient…
Improving LLM-Generated Code Quality with GRPO
Maxime Robeyns, Laurence Aitchison
Large Language Models (LLMs) are gaining widespread use for code generation. Recent training procedures use execution feedback as a reward signal, typically focusing on the functio…
A Self-Improving Coding Agent
Maxime Robeyns, Martin Szummer, Laurence Aitchison
Recent advancements in Large Language Models (LLMs) have spurred interest in deploying LLM agents to undertake tasks in the world. LLMs are often deployed in agent systems: code th…
Instruction Tuning With Loss Over Instructions
Zhengyan Shi, Adam X. Yang, Bin Wu +3
Instruction tuning plays a crucial role in shaping the outputs of language models (LMs) to desired styles. In this work, we propose a simple yet effective method, Instruction Model…
Bayesian Reward Models for LLM Alignment
Adam X. Yang, Maxime Robeyns, Thomas Coste +4
To ensure that large language model (LLM) responses are helpful and non-toxic, a reward model trained on human preference data is usually used. LLM responses with high rewards are…