790 citations · 2.7k across the 58 of their papers we have counts for
49 papers · 1 filter
Recursive Agent Optimization
Apurva Gandhi, Satyaki Chakraborty, Xiangjun Wang +2
We introduce Recursive Agent Optimization (RAO), a reinforcement learning approach for training recursive agents: agents that can spawn and delegate sub-tasks to new instantiations…
RLAC: Reinforcement Learning with Adversarial Critic for Free-Form Generation Tasks
Mian Wu, Gavin Zhang, Sewon Min +2
Open-ended generation tasks require outputs to satisfy diverse and often implicit task-specific evaluation rubrics. The sheer number of relevant rubrics leads to prohibitively high…
Horizon Reduction Makes RL Scalable
Seohong Park, Kevin Frans, Deepinder Mann +3
In this work, we study the scalability of offline reinforcement learning (RL) algorithms. In principle, a truly scalable offline RL algorithm should be able to solve any given prob…
Scaling Test-Time Compute Without Verification or RL is Suboptimal
Amrith Setlur, Nived Rajaraman, Sergey Levine +1
Despite substantial advances in scaling test-time compute, an ongoing debate in the community is how it should be scaled up to enable continued and efficient improvements with scal…
Digi-Q: Learning Q-Value Functions for Training Device-Control Agents
Hao Bai, Yifei Zhou, Li Erran Li +2
While a number of existing approaches for building foundation model agents rely on prompting or fine-tuning with human demonstrations, it is not sufficient in dynamic environments…
Value-Based Deep RL Scales Predictably
Oleh Rybkin, Michal Nauman, Preston Fu +4
Scaling data and compute is critical to the success of modern ML. However, scaling demands predictability: we want methods to not only perform well with more compute or data, but a…
Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data
Zhiyuan Zhou, Andy Peng, Qiyang Li +2
The modern paradigm in machine learning involves pre-training on diverse data, followed by task-specific fine-tuning. In reinforcement learning (RL), this translates to learning vi…
D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning
Rafael Rafailov, Kyle Hatch, Anikait Singh +9
Offline reinforcement learning algorithms hold the promise of enabling data-driven RL methods that do not require costly or dangerous real-world exploration and benefit from large…
DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning
Hao Bai, Yifei Zhou, Mert Cemri +4
Training corpuses for vision language models (VLMs) typically lack sufficient amounts of decision-centric data. This renders off-the-shelf VLMs sub-optimal for decision-making task…
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data
Fahim Tajwar, Anikait Singh, Archit Sharma +6
Learning from preference labels plays a crucial role in fine-tuning large language models. There are several distinct approaches for preference fine-tuning, including supervised le…
Is Value Learning Really the Main Bottleneck in Offline RL?
Seohong Park, Kevin Frans, Sergey Levine +1
While imitation learning requires access to high-quality data, offline reinforcement learning (RL) should, in principle, perform similarly or better with substantially lower data q…
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL
Jesse Farebrother, Jordi Orbay, Quan Vuong +9
Value functions are a central component of deep reinforcement learning (RL). These functions, parameterized by neural networks, are trained using a mean squared error regression ob…
Unfamiliar Finetuning Examples Control How Language Models Hallucinate
Katie Kang, Eric Wallace, Claire Tomlin +2
Large language models are known to hallucinate when faced with unfamiliar queries, but the underlying mechanism that govern how models hallucinate are not yet fully understood. In…
ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RL
Yifei Zhou, Andrea Zanette, Jiayi Pan +2
A broad use case of large language models (LLMs) is in goal-directed decision-making tasks (or "agent" tasks), where an LLM needs to not just generate completions for a given promp…
Vision-Language Models Provide Promptable Representations for Reinforcement Learning
William Chen, Oier Mees, Aviral Kumar +1
Humans can quickly learn new behaviors by leveraging background world knowledge. In contrast, agents trained with reinforcement learning (RL) typically learn behaviors from scratch…
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced Datasets
Zhang-Wei Hong, Aviral Kumar, Sathwik Karnik +6
Offline policy learning is aimed at learning decision-making policies using existing datasets of trajectories without collecting additional data. The primary motivation for using r…
Efficient Deep Reinforcement Learning Requires Regulating Overfitting
Qiyang Li, Aviral Kumar, Ilya Kostrikov +1
Deep reinforcement learning algorithms that learn policies by trial-and-error must learn from limited amounts of data collected by actively interacting with the environment. While…
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning
Mitsuhiko Nakamoto, Yuexiang Zhai, Anikait Singh +5
A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization from existing datasets followed by fast online fine-tuning with limited interactio…
Confidence-Conditioned Value Functions for Offline Reinforcement Learning
Joey Hong, Aviral Kumar, Sergey Levine
Offline reinforcement learning (RL) promises the ability to learn effective policies solely using existing, static datasets, without any costly online interaction. To do so, offlin…
Data-Driven Offline Decision-Making via Invariant Representation Learning
Han Qi, Yi Su, Aviral Kumar +1
The goal in offline data-driven decision-making is synthesize decisions that optimize a black-box utility function, using a previously-collected static dataset, with no active inte…
Offline RL With Realistic Datasets: Heteroskedasticity and Support Constraints
Anikait Singh, Aviral Kumar, Quan Vuong +2
Offline reinforcement learning (RL) learns policies entirely from static datasets, thereby avoiding the challenges associated with online data collection. Practical applications of…
Dual Generator Offline Reinforcement Learning
Quan Vuong, Aviral Kumar, Sergey Levine +1
In offline RL, constraining the learned policy to remain close to the data is essential to prevent the policy from outputting out-of-distribution (OOD) actions with erroneously ove…
Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes
Aviral Kumar, Rishabh Agarwal, Xinyang Geng +2
The potential of offline reinforcement learning (RL) is that high-capacity models trained on large, heterogeneous datasets can lead to agents that generalize broadly, analogously t…
When Should We Prefer Offline Reinforcement Learning Over Behavioral Cloning?
Aviral Kumar, Joey Hong, Anikait Singh +1
Offline reinforcement learning (RL) algorithms can acquire effective policies by utilizing previously collected experience, without any online interaction. It is widely understood…