12 citations · 67 across the 68 of their papers we have counts for
19 papers · 1 filter
On the geometry and topology of representations: the manifolds of modular addition
Gabriela Moisescu-Pareja, Gavin McCracken, Harley Wiltzer +4
The Clock and Pizza interpretations, associated with architectures differing in either uniform or learnable attention, were introduced to argue that different architectural designs…
On Mobile Ad Hoc Networks for Coverage of Partially Observable Worlds
Edwin Meriaux, Shuo Wen, Louis-Roy Langevin +3
This paper addresses the movement and placement of mobile agents to establish a communication network in initially unknown environments. We cast the problem in a computational-geom…
Incorporating Spatial Information into Goal-Conditioned Hierarchical Reinforcement Learning via Graph Representations
Shuyuan Zhang, Zihan Wang, Xiao-Wen Chang +1
The integration of graphs with Goal-conditioned Hierarchical Reinforcement Learning (GCHRL) has recently gained attention, as intermediate goals (subgoals) can be effectively sampl…
RL Fine-Tuning Heals OOD Forgetting in SFT
Hangzhan Jin, Sitao Luan, Tianwei Ni +5
Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) is a standard post-training recipe for improving Large Language Models (LLM) reasoning, but why it works remain…
Relative Trajectory Balance is equivalent to Trust-PCL
Tristan Deleu, Padideh Nouri, Yoshua Bengio +1
Recent progress in generative modeling has highlighted the importance of Reinforcement Learning (RL) for fine-tuning, with KL-regularized methods in particular proving to be highly…
Capacity-Constrained Continual Learning
Zheng Wen, Doina Precup, Benjamin Van Roy +1
Any agents we can possibly build are subject to capacity constraints, as memory and compute resources are inherently finite. However, comparatively little attention has been dedica…