8 citations · 24 across the 14 of their papers we have counts for
16 papers
From Prompts to Posteriors: Can LLM Agents Perform Bayesian Inference in Context?
Linus Filbry, Luckeciano C. Melo, Yarin Gal
Large language models (LLMs) are increasingly applied across the scientific discovery pipeline, from synthesizing literature to generating hypotheses to informing experimental desi…
Amortising Bayesian Experimental Design for Sequential Information Gathering in LLMs
Jakob Hartmann, James Harvey, Jhonathan Navott +5
Large language models (LLMs) exhibit strong reasoning and world-knowledge capabilities, yet often struggle to gather information effectively across the multi-turn interactions requ…
Partial Reasoning in Language Models: Search and Refinement Guided by Uncertainty
Murilo da Luz, Bruno Brandão, Luana Martins +4
The use of Large Language Models (LLMs) for reasoning and planning tasks has drawn increasing attention in Artificial Intelligence research. Despite their remarkable progress, thes…
Iterative Deployment Improves Planning Skills in LLMs
Augusto B. Corrêa, Yoav Gelberg, Luckeciano C. Melo +3
We show that iterative deployment of large language models (LLMs), each fine-tuned on data carefully curated by users from the previous models' deployment, can significantly change…
Learning Without Critics? Revisiting GRPO in Classical Reinforcement Learning Environments
Bryan L. M. de Oliveira, Felipe V. Frujeri, Marcos P. C. M. Queiroz +3
Group Relative Policy Optimization (GRPO) has emerged as a scalable alternative to Proximal Policy Optimization (PPO) by eliminating the learned critic and instead estimating advan…
Stabilizing Policy Gradients for Sample-Efficient Reinforcement Learning in LLM Reasoning
Luckeciano C. Melo, Alessandro Abate, Yarin Gal
Reinforcement Learning, particularly through policy gradient methods, has played a central role in enabling reasoning capabilities of Large Language Models. However, the optimizati…
Uncertainty-Aware Step-wise Verification with Generative Reward Models
Zihuiwen Ye, Luckeciano Carvalho Melo, Younesse Kaddar +3
Complex multi-step reasoning tasks, such as solving mathematical problems, remain challenging for large language models (LLMs). While outcome supervision is commonly used, process…
InfoQuest: Evaluating Multi-Turn Dialogue Agents for Open-Ended Conversations with Hidden Context
Bryan L. M. de Oliveira, Luana G. B. Martins, Bruno Brandão +1
Large language models excel at following explicit instructions, but they often struggle with ambiguous or incomplete user requests, defaulting to verbose, generic responses instead…
Sliding Puzzles Gym: A Scalable Benchmark for State Representation in Visual Reinforcement Learning
Bryan L. M. de Oliveira, Luana G. B. Martins, Bruno Brandão +3
Effective visual representation learning is crucial for reinforcement learning (RL) agents to extract task-relevant information from raw sensory inputs and generalize across divers…
Temporal-Difference Variational Continual Learning
Luckeciano C. Melo, Alessandro Abate, Yarin Gal
Machine Learning models in real-world applications must continuously learn new tasks to adapt to shifts in the data-generating distribution. Yet, for Continual Learning (CL), model…
Deep Bayesian Active Learning for Preference Modeling in Large Language Models
Luckeciano C. Melo, Panagiotis Tigas, Alessandro Abate +1
Leveraging human preferences for steering the behavior of Large Language Models (LLMs) has demonstrated notable success in recent years. Nonetheless, data selection and labeling ar…
Transformers are Meta-Reinforcement Learners
Luckeciano C. Melo
The transformer architecture and variants presented remarkable success across many machine learning tasks in recent years. This success is intrinsically related to the capability o…
MARS-Gym: A Gym framework to model, train, and evaluate Recommender Systems for Marketplaces
Marlesson R. O. Santana, Luckeciano C. Melo, Fernando H. F. Camargo +4
Recommender Systems are especially challenging for marketplaces since they must maximize user satisfaction while maintaining the healthiness and fairness of such ecosystems. In thi…
Learning Humanoid Robot Running Skills through Proximal Policy Optimization
Luckeciano C. Melo, Marcos R. O. A. Maximo
In the current level of evolution of Soccer 3D, motion control is a key factor in team's performance. Recent works takes advantages of model-free approaches based on Machine Learni…
Bottom-Up Meta-Policy Search
Luckeciano C. Melo, Marcos R. O. A. Maximo, Adilson Marques da Cunha
Despite of the recent progress in agents that learn through interaction, there are several challenges in terms of sample efficiency and generalization across unseen behaviors durin…
Learning Humanoid Robot Motions Through Deep Neural Networks
Luckeciano Carvalho Melo, Marcos Ricardo Omena Albuquerque Maximo, Adilson Marques da Cunha
Controlling a high degrees of freedom humanoid robot is acknowledged as one of the hardest problems in Robotics. Due to the lack of mathematical models, an approach frequently empl…