18 citations · 42 across the 23 of their papers we have counts for
24 papers
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…
OpenAI GPT-5 System Card
Aaditya Singh, Adam Fry, Adam Perelman +483
This is the system card published alongside the OpenAI GPT-5 launch, August 2025. GPT-5 is a unified system with a smart and fast model that answers most questions, a deeper reason…
gpt-oss-120b & gpt-oss-20b Model Card
OpenAI, :, Sandhini Agarwal +124
We present gpt-oss-120b and gpt-oss-20b, two open-weight reasoning models that push the frontier of accuracy and inference cost. The models use an efficient mixture-of-expert trans…
CogniSQL-R1-Zero: Lightweight Reinforced Reasoning for Efficient SQL Generation
Kushal Gajjar, Harshit Sikchi, Arpit Singh Gautam +2
Translating natural language into SQL (Text-to-SQL) remains a core challenge at the intersection of language understanding and structured data access. Although large language model…
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…
CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance
Arthur Zhang, Harshit Sikchi, Amy Zhang +1
We introduce CREStE, a scalable learning-based mapless navigation framework to address the open-world generalization and robustness challenges of outdoor urban navigation. Key to a…
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…
Proto Successor Measure: Representing the Behavior Space of an RL Agent
Siddhant Agarwal, Harshit Sikchi, Peter Stone +1
Having explored an environment, intelligent agents should be able to transfer their knowledge to most downstream tasks within that environment without additional interactions. Refe…
PERSONA: A Reproducible Testbed for Pluralistic Alignment
Louis Castricato, Nathan Lile, Rafael Rafailov +2
The rapid advancement of language models (LMs) necessitates robust alignment with diverse user values. However, current preference optimization approaches often fail to capture the…
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…
Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms
Rafael Rafailov, Yaswanth Chittepu, Ryan Park +5
Reinforcement Learning from Human Feedback (RLHF) has been crucial to the recent success of Large Language Models (LLMs), however, it is often a complex and brittle process. In the…
Robot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learning
Caleb Chuck, Carl Qi, Michael J. Munje +13
Reinforcement Learning is a promising tool for learning complex policies even in fast-moving and object-interactive domains where human teleoperation or hard-coded policies might f…
SMORE: Score Models for Offline Goal-Conditioned Reinforcement Learning
Harshit Sikchi, Rohan Chitnis, Ahmed Touati +3
Offline Goal-Conditioned Reinforcement Learning (GCRL) is tasked with learning to achieve multiple goals in an environment purely from offline datasets using sparse reward function…
Contrastive Preference Learning: Learning from Human Feedback without RL
Joey Hejna, Rafael Rafailov, Harshit Sikchi +4
Reinforcement Learning from Human Feedback (RLHF) has emerged as a popular paradigm for aligning models with human intent. Typically RLHF algorithms operate in two phases: first, u…
Dual RL: Unification and New Methods for Reinforcement and Imitation Learning
Harshit Sikchi, Qinqing Zheng, Amy Zhang +1
The goal of reinforcement learning (RL) is to find a policy that maximizes the expected cumulative return. It has been shown that this objective can be represented as an optimizati…
Real Robot Challenge 2021: Cartesian Position Control with Triangle Grasp and Trajectory Interpolation
Rishabh Madan, Harshit Sikchi, Ethan K. Gordon +1
We present our runner-up approach for the Real Robot Challenge 2021. We build upon our previous approach used in Real Robot Challenge 2020. To solve the task of sequential goal-rea…
A Ranking Game for Imitation Learning
Harshit Sikchi, Akanksha Saran, Wonjoon Goo +1
We propose a new framework for imitation learning -- treating imitation as a two-player ranking-based game between a policy and a reward. In this game, the reward agent learns to s…
Real Robot Challenge: A Robotics Competition in the Cloud
Stefan Bauer, Felix Widmaier, Manuel Wüthrich +39
Dexterous manipulation remains an open problem in robotics. To coordinate efforts of the research community towards tackling this problem, we propose a shared benchmark. We designe…
Lyapunov Barrier Policy Optimization
Harshit Sikchi, Wenxuan Zhou, David Held
Deploying Reinforcement Learning (RL) agents in the real-world require that the agents satisfy safety constraints. Current RL agents explore the environment without considering the…
f-IRL: Inverse Reinforcement Learning via State Marginal Matching
Tianwei Ni, Harshit Sikchi, Yufei Wang +3
Imitation learning is well-suited for robotic tasks where it is difficult to directly program the behavior or specify a cost for optimal control. In this work, we propose a method…
Learning Off-Policy with Online Planning
Harshit Sikchi, Wenxuan Zhou, David Held
Reinforcement learning (RL) in low-data and risk-sensitive domains requires performant and flexible deployment policies that can readily incorporate constraints during deployment.…
Imitative Planning using Conditional Normalizing Flow
Shubhankar Agarwal, Harshit Sikchi, Cole Gulino +2
A popular way to plan trajectories in dynamic urban scenarios for Autonomous Vehicles is to rely on explicitly specified and hand crafted cost functions, coupled with random sampli…
Illumination-invariant Face recognition by fusing thermal and visual images via gradient transfer
Sumit Agarwal, Harshit S. Sikchi, Suparna Rooj +2
Face recognition in real life situations like low illumination condition is still an open challenge in biometric security. It is well established that the state-of-the-art methods…