Tootfinder

This https://arxiv.org/abs/2503.04280 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Towards Autonomous Reinforcement Learning for Real-World Robotic Manipulation with Large Language Models
Recent advancements in Large Language Models (LLMs) and Visual Language Models (VLMs) have significantly impacted robotics, enabling high-level semantic motion planning applications. Reinforcement Learning (RL), a complementary paradigm, enables agents to autonomously optimize complex behaviors through interaction and reward signals. However, designing effective reward functions for RL remains challenging, especially in real-world tasks where sparse rewards are insufficient and dense rewards re…

Structured Prompts, Better Outcomes? Exploring the Effects of a Structured Interface with ChatGPT in a Graduate Robotics Course
Jerome Brender, Laila El-Hamamsy, Kim Uittenhove, Francesco Mondada, Engin Bumbacher
https://arxiv.org/abs/2507.07767

Structured Prompts, Better Outcomes? Exploring the Effects of a Structured Interface with ChatGPT in a Graduate Robotics Course
Prior research shows that how students engage with Large Language Models (LLMs) influences their problem-solving and understanding, reinforcing the need to support productive LLM-uses that promote learning. This study evaluates the impact of a structured GPT platform designed to promote 'good' prompting behavior with data from 58 students in a graduate-level robotics course. The students were assigned to either an intervention group using the structured platform or a control group using ChatGPT…

Personalized Large Language Models Can Increase the Belief Accuracy of Social Networks
Adiba Mahbub Proma, Neeley Pate, Sean Kelty, Gourab Ghoshal, James N. Druckman, Ehsan Hoque
https://arxiv.org/abs/2506.06153

Personalized Large Language Models Can Increase the Belief Accuracy of Social Networks
Large language models (LLMs) are increasingly involved in shaping public understanding on contested issues. This has led to substantial discussion about the potential of LLMs to reinforce or correct misperceptions. While existing literature documents the impact of LLMs on individuals' beliefs, limited work explores how LLMs affect social networks. We address this gap with a pre-registered experiment (N = 1265) around the 2024 US presidential election, where we empirically explore the impact of …

polyBART: A Chemical Linguist for Polymer Property Prediction and Generative Design
Anagha Savit, Harikrishna Sahu, Shivank Shukla, Wei Xiong, Rampi Ramprasad
https://arxiv.org/abs/2506.04233

polyBART: A Chemical Linguist for Polymer Property Prediction and Generative Design
Designing polymers for targeted applications and accurately predicting their properties is a key challenge in materials science owing to the vast and complex polymer chemical space. While molecular language models have proven effective in solving analogous problems for molecular discovery, similar advancements for polymers are limited. To address this gap, we propose polyBART, a language model-driven polymer discovery capability that enables rapid and accurate exploration of the polymer design …

This https://arxiv.org/abs/2505.23585 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

On-Policy RL with Optimal Reward Baseline
Reinforcement learning algorithms are fundamental to align large language models with human preferences and to enhance their reasoning capabilities. However, current reinforcement learning algorithms often suffer from training instability due to loose on-policy constraints and computational inefficiency due to auxiliary models. In this work, we propose On-Policy RL with Optimal reward baseline (OPO), a novel and simplified reinforcement learning algorithm designed to address these challenges. O…

Self-correcting Reward Shaping via Language Models for Reinforcement Learning Agents in Games
Ant\'onio Afonso, Iolanda Leite, Alessandro Sestini, Florian Fuchs, Konrad Tollmar, Linus Gissl\'en
https://arxiv.org/abs/2506.23626

Self-correcting Reward Shaping via Language Models for Reinforcement Learning Agents in Games
Reinforcement Learning (RL) in games has gained significant momentum in recent years, enabling the creation of different agent behaviors that can transform a player's gaming experience. However, deploying RL agents in production environments presents two key challenges: (1) designing an effective reward function typically requires an RL expert, and (2) when a game's content or mechanics are modified, previously tuned reward weights may no longer be optimal. Towards the latter challenge, we prop…

Benchmarking Large Language Models for Polymer Property Predictions
Sonakshi Gupta, Akhlak Mahmood, Shivank Shukla, Rampi Ramprasad
https://arxiv.org/abs/2506.02129

Benchmarking Large Language Models for Polymer Property Predictions
Machine learning has revolutionized polymer science by enabling rapid property prediction and generative design. Large language models (LLMs) offer further opportunities in polymer informatics by simplifying workflows that traditionally rely on large labeled datasets, handcrafted representations, and complex feature engineering. LLMs leverage natural language inputs through transfer learning, eliminating the need for explicit fingerprinting and streamlining training. In this study, we finetune …

AURA: Agentic Upskilling via Reinforced Abstractions
Alvin Zhu, Yusuke Tanaka, Dennis Hong
https://arxiv.org/abs/2506.02507 https://a…

AURA: Agentic Upskilling via Reinforced Abstractions
We study the combinatorial explosion involved in translating high-level task prompts into deployable control policies for agile robots through multi-stage reinforcement learning. We introduce AURA (Agentic Upskilling via Reinforced Abstractions), a schema-centric curriculum RL framework that leverages Large Language Models (LLMs) as autonomous designers of multi-stage curricula. AURA transforms user prompts into YAML workflows that encode full reward functions, domain randomization strategies, …

LAMARL: LLM-Aided Multi-Agent Reinforcement Learning for Cooperative Policy Generation
Guobin Zhu, Rui Zhou, Wenkang Ji, Shiyu Zhao
https://arxiv.org/abs/2506.01538

LAMARL: LLM-Aided Multi-Agent Reinforcement Learning for Cooperative Policy Generation
Although Multi-Agent Reinforcement Learning (MARL) is effective for complex multi-robot tasks, it suffers from low sample efficiency and requires iterative manual reward tuning. Large Language Models (LLMs) have shown promise in single-robot settings, but their application in multi-robot systems remains largely unexplored. This paper introduces a novel LLM-Aided MARL (LAMARL) approach, which integrates MARL with LLMs, significantly enhancing sample efficiency without requiring manual design. LA…

This https://arxiv.org/abs/2506.01538 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

LAMARL: LLM-Aided Multi-Agent Reinforcement Learning for Cooperative Policy Generation
Although Multi-Agent Reinforcement Learning (MARL) is effective for complex multi-robot tasks, it suffers from low sample efficiency and requires iterative manual reward tuning. Large Language Models (LLMs) have shown promise in single-robot settings, but their application in multi-robot systems remains largely unexplored. This paper introduces a novel LLM-Aided MARL (LAMARL) approach, which integrates MARL with LLMs, significantly enhancing sample efficiency without requiring manual design. LA…