3 citations · 8 across the 13 of their papers we have counts for
7 papers · 1 filter
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…