Exploring Model Graders for Reinforcement Fine-Tuning

Understanding Reinforcement Learning for Model Training, and future directions with GRAPE

Reinforcement Learning to Train Large Language Models to Explain Human Decisions

Supervised Fine-Tuning as Reinforcement Learning: Introducing Importance-Weighted SFT

The article explores the connection between supervised fine-tuning (SFT) of large language models and reinforcement learning (RL), arguing t

JAMEL: A Framework for Joint Memory and Exploration Learning in Language Model Agents

This paper introduces JAMEL (Joint Agent Memory and Exploration Learning), a framework that trains language model agents to explore open-end

Study reveals why in-context learning fails on complex specification-heavy tasks and how fine-tuning can help

This research paper investigates the limitations of in-context learning (ICL) for large language models (LLMs) when applied to specification

Localizing Factual Recall Circuits in Gemma Models via Activation Patching

This article presents BizzaroWorld, a mechanistic interpretability study that localizes factual recall circuits in the Gemma-2B and Gemma-12