Anikait Singh

According to our database1, Anikait Singh authored at least 17 papers between 2021 and 2024.

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Bibliography

2024
Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation.
CoRR, 2024

D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning.
RLJ, 2024

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration.
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Proceedings of the IEEE International Conference on Robotics and Automation, 2024

Robotic Offline RL from Internet Videos via Value-Function Learning.
Proceedings of the IEEE International Conference on Robotics and Automation, 2024

Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Open X-Embodiment: Robotic Learning Datasets and RT-X Models.
CoRR, 2023

Robotic Offline RL from Internet Videos via Value-Function Pre-Training.
CoRR, 2023

Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning.
CoRR, 2023

Pre-Training for Robots: Offline RL Enables Learning New Tasks in a Handful of Trials.
Proceedings of the Robotics: Science and Systems XIX, Daegu, 2023

ReDS: Offline RL With Heteroskedastic Datasets via Support Constraints.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023


2022
Offline RL With Realistic Datasets: Heteroskedasticity and Support Constraints.
CoRR, 2022

Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials.
CoRR, 2022

When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?
CoRR, 2022

Should I Run Offline Reinforcement Learning or Behavioral Cloning?
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
A Workflow for Offline Model-Free Robotic Reinforcement Learning.
Proceedings of the Conference on Robot Learning, 8-11 November 2021, London, UK., 2021


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