3 citations · 8 across the 13 of their papers we have counts for
14 papers
iSDFT: Information-Proximal Self-Distillation for Continual Learning in LLMs
Ahmed Khaled Khamis, Xiaotong Ji, Hassan Jaber +4
On-policy self-distillation fine-tuning (SDFT) learns new skills from demonstrations while reducing forgetting, but it always distils toward the full demonstration-conditioned teac…
Model-Based and Sample-Efficient AI-Assisted Math Discovery in Sphere Packing
Rasul Tutunov, Alexandre Maraval, Antoine Grosnit +3
Sphere packing, Hilbert's eighteenth problem, asks for the densest arrangement of congruent spheres in n-dimensional Euclidean space. Although relevant to areas such as cryptograph…
Ark: An Open-source Python-based Framework for Robot Learning
Magnus Dierking, Christopher E. Mower, Sarthak Das +10
Robotics has made remarkable hardware strides-from DARPA's Urban and Robotics Challenges to the first humanoid-robot kickboxing tournament-yet commercial autonomy still lags behind…
Bourbaki: Self-Generated and Goal-Conditioned MDPs for Theorem Proving
Matthieu Zimmer, Xiaotong Ji, Rasul Tutunov +3
Reasoning remains a challenging task for large language models (LLMs), especially within the logically constrained environment of automated theorem proving (ATP), due to sparse rew…
On Almost Surely Safe Alignment of Large Language Models at Inference-Time
Xiaotong Ji, Shyam Sundhar Ramesh, Matthieu Zimmer +3
We introduce a novel inference-time alignment approach for LLMs that aims to generate safe responses almost surely, i.e., with probability approaching one. Our approach models the…
Kolb-Based Experiential Learning for Generalist Agents with Human-Level Kaggle Data Science Performance
Antoine Grosnit, Alexandre Maraval, Refinath S N +16
Human expertise emerges through iterative cycles of interaction, reflection, and internal model updating, which are central to cognitive theories such as Kolb's experiential learni…
SparsePO: Controlling Preference Alignment of LLMs via Sparse Token Masks
Fenia Christopoulou, Ronald Cardenas, Gerasimos Lampouras +2
Direct alignment algorithms have proven an effective step for aligning language models to human-desired behaviors. Current variants of the Direct Preference Optimization objective…
Mixture of Attentions For Speculative Decoding
Matthieu Zimmer, Milan Gritta, Gerasimos Lampouras +2
The growth in the number of parameters of Large Language Models (LLMs) has led to a significant surge in computational requirements, making them challenging and costly to deploy. S…
ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning
Christopher E. Mower, Yuhui Wan, Hongzhan Yu +20
We present a framework for intuitive robot programming by non-experts, leveraging natural language prompts and contextual information from the Robot Operating System (ROS). Our sys…
Human-inspired Episodic Memory for Infinite Context LLMs
Zafeirios Fountas, Martin A Benfeghoul, Adnan Oomerjee +4
Large language models (LLMs) have shown remarkable capabilities, but still struggle with processing extensive contexts, limiting their ability to maintain coherence and accuracy ov…
ZSL-RPPO: Zero-Shot Learning for Quadrupedal Locomotion in Challenging Terrains using Recurrent Proximal Policy Optimization
Yao Zhao, Tao Wu, Yijie Zhu +5
We present ZSL-RPPO, an improved zero-shot learning architecture that overcomes the limitations of teacher-student neural networks and enables generating robust, reliable, and vers…
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…
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
Filippos Christianos, Georgios Papoudakis, Matthieu Zimmer +13
A key method for creating Artificial Intelligence (AI) agents is Reinforcement Learning (RL). However, constructing a standalone RL policy that maps perception to action directly e…
Why Can Large Language Models Generate Correct Chain-of-Thoughts?
Rasul Tutunov, Antoine Grosnit, Juliusz Ziomek +2
This paper delves into the capabilities of large language models (LLMs), specifically focusing on advancing the theoretical comprehension of chain-of-thought prompting. We investig…