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Notes on reinforcement learning
128 notes, page 6 of 6
LaGO: Latent Action Guidance for Online Reinforcement Learning
The paper 'LaGO: Latent Action Guidance for Online Reinforcement Learning' proposes a new framework called LaGO. This framework uses a pre-trained language model (LLM) as a guide to help with online policy optimization…
Reinforcement Learning for Computer-Use Agents with Autonomous Evaluation
Researchers propose a framework for training Computer-Use Agents (CUAs) using reinforcement learning and autonomous vision-language evaluation. This method provides a scalable supervision signal, improving success…
Themis: An explainable AI-enabled framework for Reinforcement Learning with Human Feedback
Researchers present Themis, an AI framework that combines explainability and human feedback to train safe Reinforcement Learning (RL) systems. The framework is designed for over 200 environments, easy configuration for…
AIR: Adaptive Interleaved Reasoning with Code in MLLMs
The paper presents a new approach to enhance multimodal large language models (MLLMs) by giving them the ability to reason adaptively with code, particularly in numerical computation tasks. This is achieved through…
Decentralized Autonomous Traffic Management through Corridor Networks
Researchers propose a decentralized approach to managing traffic in Advanced Air Mobility (AAM) corridors using multi-agent reinforcement learning (MARL). The method is tested on various multi-corridor networks and…
SPIRAL: Learning to Search and Aggregate
Researchers introduced SPIRAL, a framework for improving language model reasoning at test time. The framework uses three primitives - sequential reasoning within a trace, independently sampled parallel traces, and…
VeriEvol: Scaling Multimodal Mathematical Reasoning via Verifiable Evol-Instruct
The paper introduces VeriEvol, a framework that addresses scaling issues in reinforcement learning for visual mathematical reasoning. The framework decouples the difficulty of prompts and the reliability of answers…
VeriEvol: Scaling Multimodal Mathematical Reasoning via Verifiable Evol-Instruct
The paper presents VeriEvol, a framework for scaling reinforcement learning in visual mathematical reasoning. It addresses the challenge of maintaining reliable reward labels as data volume grows by treating scaling as…
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