4 papers
Bootstrapped Mixed Rewards for RL Post-Training: Injecting Canonical Action Order
Prakhar Gupta, Vaibhav Gupta
Post-training with reinforcement learning (RL) typically optimizes a single scalar objective and ignores structure in how solutions are produced. We ask whether a scalar hint towar…
Better World Models Can Lead to Better Post-Training Performance
Prakhar Gupta, Henry Conklin, Sarah-Jane Leslie +1
In this work we study how explicit world-modeling objectives affect the internal representations and downstream capability of Transformers across different training stages. We use…
Revisiting In-Context Learning with Long Context Language Models
Jinheon Baek, Sun Jae Lee, Prakhar Gupta +3
In-Context Learning (ICL) is a technique by which language models make predictions based on examples provided in their input context. Previously, their context window size imposed…
Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions
Angana Borah, Rada Mihalcea
As Large Language Models (LLMs) continue to evolve, they are increasingly being employed in numerous studies to simulate societies and execute diverse social tasks. However, LLMs a…