Rishabh Agarwal

Affiliations

Google Research, Brain Team

papers

2,209

total citations

papers (12)

Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research

NEURIPS 2023arXiv

163

citations

Stop Regressing: Training Value Functions via Classification for Scalable Deep RL

ICML 2024arXiv

107

citations

Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress

NEURIPS 2022arXiv

citations

RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning

NEURIPS 2020arXiv

citations

Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models

ICLR 2025arXiv

citations

Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models

ICLR 2025arXiv

citations

Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers

COLM 2025arXiv

citations

SiT: Symmetry-invariant Transformers for Generalisation in Reinforcement Learning

ICML 2024arXiv

citations

Towards Compute-Optimal Many-Shot In-Context Learning

COLM 2025arXiv

citations

Rishabh Agarwal

Affiliations

papers (12)

Deep Reinforcement Learning at the Edge of the Statistical Precipice

Neural Additive Models: Interpretable Machine Learning with Neural Nets

Training Language Models to Self-Correct via Reinforcement Learning

Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research

Stop Regressing: Training Value Functions via Classification for Scalable Deep RL

Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress

RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning

Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models

Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models

Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers

SiT: Symmetry-invariant Transformers for Generalisation in Reinforcement Learning

Towards Compute-Optimal Many-Shot In-Context Learning

papers (12)

Deep Reinforcement Learning at the Edge of the Statistical Precipice

Neural Additive Models: Interpretable Machine Learning with Neural Nets

Training Language Models to Self-Correct via Reinforcement Learning

Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research

Stop Regressing: Training Value Functions via Classification for Scalable Deep RL

Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress

RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning

Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models

Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models

Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers

SiT: Symmetry-invariant Transformers for Generalisation in Reinforcement Learning

Towards Compute-Optimal Many-Shot In-Context Learning