96 citations · 359 across the 71 of their papers we have counts for
78 papers
Towards Continual Learning: Mitigating Forgetting With Subspace Tuning
Arnav Chavan, Kusha Sareen, Rishabh Tiwari +6
Large language models (LLMs) acquire new skills through fine-tuning, but it can destroy capabilities they already have: catastrophic forgetting remains a central obstacle to contin…
Convex-Concave Reinforcement Learning
Shripad V. Deshmukh, Yaswanth Chittepu, Dhawal Gupta +2
Policy learning drives many of the most consequential and heavily-invested applications of reinforcement learning today. Yet the core optimization problem it rests on (maximizing e…
Safe Inference-Time Alignment via Lagrangian Reward Augmentation
Yaswanth Chittepu, Ativ Joshi, Sohini Chintala +1
Inference-time alignment steers a frozen language model during decoding using auxiliary reward signals, avoiding the cost of repeated weight updates. However, existing inference-ti…
Hierarchical Experimentalist Agents
Abhranil Chandra, Sankaran Vaidyanathan, Utsav Dhanuka +2
Large language models (LLMs) are increasingly used to act in the real world and augment human decision-making, yet most systems rely on parametric knowledge acquired through imitat…
The Curse of Multiple Mediators: Hidden Interaction Effects in Activation Patching
Sankaran Vaidyanathan, David Arbour, Aaron Mueller +2
Activation patching is the primary tool in mechanistic interpretability. It attributes causal responsibility for a model behavior to each of its individual components by estimating…
Training ML Models with Predictable Failures
Will Schwarzer, Scott Niekum
Estimating how often an ML model will fail at deployment scale is central to pre-deployment safety assessment, but a feasible evaluation set is rarely large enough to observe the f…
Regularized Latent Dynamics Prediction is a Strong Baseline For Behavioral Foundation Models
Pranaya Jajoo, Harshit Sikchi, Siddhant Agarwal +3
Behavioral Foundation Models (BFMs) produce agents with the capability to adapt to any unknown reward or task. These methods, however, are only able to produce near-optimal policie…
Safe RLHF Beyond Expectation: Stochastic Dominance for Universal Spectral Risk Control
Yaswanth Chittepu, Ativ Joshi, Rajarshi Bhattacharjee +1
Safe Reinforcement Learning from Human Feedback (RLHF) typically enforces safety through expected cost constraints, but the expectation captures only a single statistic of the cost…
Adaptive Margin RLHF via Preference over Preferences
Yaswanth Chittepu, Prasann Singhal, Greg Durrett +1
Margin-based optimization is fundamental to improving generalization and robustness in classification tasks. In the context of reward model learning from preferences within Reinfor…
Evaluation-Aware Reinforcement Learning
Shripad Vilasrao Deshmukh, Will Schwarzer, Scott Niekum
Policy evaluation is a core component of many reinforcement learning (RL) algorithms and a critical tool for ensuring safe deployment of RL policies. However, existing policy evalu…
DeepFleet: Multi-Agent Foundation Models for Mobile Robots
Ameya Agaskar, Sriram Siva, William Pickering +18
We introduce DeepFleet, a suite of foundation models designed to support coordination and planning for large-scale mobile robot fleets. These models are trained on fleet movement d…
Reinforcement Learning from Human Feedback with High-Confidence Safety Constraints
Yaswanth Chittepu, Blossom Metevier, Will Schwarzer +3
Existing approaches to language model alignment often treat safety as a tradeoff against helpfulness, which can lead to unacceptable responses in sensitive domains. To ensure relia…
A Descriptive and Normative Theory of Human Beliefs in RLHF
Sylee Dandekar, Shripad Deshmukh, Frank Chiu +2
Human preferences in RLHF are typically modeled as a function of the human's reward function or corresponding optimal state-action values. In this work, we propose that human belie…
Null Counterfactual Factor Interactions for Goal-Conditioned Reinforcement Learning
Caleb Chuck, Fan Feng, Carl Qi +4
Hindsight relabeling is a powerful tool for overcoming sparsity in goal-conditioned reinforcement learning (GCRL), especially in certain domains such as navigation and locomotion.…
An Optimal Discriminator Weighted Imitation Perspective for Reinforcement Learning
Haoran Xu, Shuozhe Li, Harshit Sikchi +2
We introduce Iterative Dual Reinforcement Learning (IDRL), a new method that takes an optimal discriminator-weighted imitation view of solving RL. Our method is motivated by a simp…
Fast Adaptation with Behavioral Foundation Models
Harshit Sikchi, Andrea Tirinzoni, Ahmed Touati +6
Unsupervised zero-shot reinforcement learning (RL) has emerged as a powerful paradigm for pretraining behavioral foundation models (BFMs), enabling agents to solve a wide range of…
Pairwise or Pointwise? Evaluating Feedback Protocols for Bias in LLM-Based Evaluation
Tuhina Tripathi, Manya Wadhwa, Greg Durrett +1
Large Language Models (LLMs) are widely used as proxies for human labelers in both training (Reinforcement Learning from AI Feedback) and large-scale response evaluation (LLM-as-a-…
Supervised Reward Inference
Will Schwarzer, Jordan Schneider, Philip S. Thomas +1
Existing approaches to reward inference typically assume that humans provide demonstrations according to specific behavior models. However, humans often indicate their goals throug…
Influencing Humans to Conform to Preference Models for RLHF
Stephane Hatgis-Kessell, W. Bradley Knox, Serena Booth +1
Designing a reinforcement learning from human feedback (RLHF) algorithm to approximate a human's unobservable reward function requires assuming, implicitly or explicitly, a model o…
RLZero: Direct Policy Inference from Language Without In-Domain Supervision
Harshit Sikchi, Siddhant Agarwal, Pranaya Jajoo +6
The reward hypothesis states that all goals and purposes can be understood as the maximization of a received scalar reward signal. However, in practice, defining such a reward sign…
Predicting Future Actions of Reinforcement Learning Agents
Stephen Chung, Scott Niekum, David Krueger
As reinforcement learning agents become increasingly deployed in real-world scenarios, predicting future agent actions and events during deployment is important for facilitating be…
SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions
Zizhao Wang, Jiaheng Hu, Caleb Chuck +5
Unsupervised skill discovery carries the promise that an intelligent agent can learn reusable skills through autonomous, reward-free environment interaction. Existing unsupervised…
Pareto-Optimal Learning from Preferences with Hidden Context
Ryan Bahlous-Boldi, Li Ding, Lee Spector +1
Ensuring AI models align with human values is essential for their safety and functionality. Reinforcement learning from human feedback (RLHF) leverages human preferences to achieve…
A Dual Approach to Imitation Learning from Observations with Offline Datasets
Harshit Sikchi, Caleb Chuck, Amy Zhang +1
Demonstrations are an effective alternative to task specification for learning agents in settings where designing a reward function is difficult. However, demonstrating expert beha…