158 citations · 534 across the 33 of their papers we have counts for
6 papers · 1 filter
How Reasoning Evolves from Post-Training Data: An Empirical Study Using Chess
Lucas Dionisopoulos, Nicklas Majamaki, Prithviraj Ammanabrolu
We study how reasoning evolves in a language model -- from supervised fine-tuning (SFT) to reinforcement learning (RL) -- by analyzing how a set of theoretically-inspired datasets…
DeltaPrompts: Escaping the Zero-Delta Trap in Multimodal Distillation
Jaehun Jung, Hyunwoo Kim, Brandon Cui +4
Distillation enables compact Vision-Language Models (VLMs) to obtain strong reasoning capabilities, yet the prompts driving this process are typically chosen via simple heuristics…
Do Embodied Agents Dream of Pixelated Sheep: Embodied Decision Making using Language Guided World Modelling
Kolby Nottingham, Prithviraj Ammanabrolu, Alane Suhr +4
Reinforcement learning (RL) agents typically learn tabula rasa, without prior knowledge of the world. However, if initialized with knowledge of high-level subgoals and transitions…
Learning Knowledge Graph-based World Models of Textual Environments
Prithviraj Ammanabrolu, Mark O. Riedl
World models improve a learning agent's ability to efficiently operate in interactive and situated environments. This work focuses on the task of building world models of text-base…
How To Avoid Being Eaten By a Grue: Exploration Strategies for Text-Adventure Agents
Prithviraj Ammanabrolu, Ethan Tien, Zhaochen Luo +1
Text-based games -- in which an agent interacts with the world through textual natural language -- present us with the problem of combinatorially-sized action-spaces. Most current…
Graph Constrained Reinforcement Learning for Natural Language Action Spaces
Prithviraj Ammanabrolu, Matthew Hausknecht
Interactive Fiction games are text-based simulations in which an agent interacts with the world purely through natural language. They are ideal environments for studying how to ext…