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Conservative Objective Models for Effective Offline Model-Based Optimization.

, , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 10358-10368. PMLR, (2021)

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Unfamiliar Finetuning Examples Control How Language Models Hallucinate., , , , and . CoRR, (2024)Chaining Behaviors from Data with Model-Free Reinforcement Learning., , , , , and . CoRL, volume 155 of Proceedings of Machine Learning Research, page 2162-2177. PMLR, (2020)RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Fold., , , , , and . CoRR, (2024)Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions., , , , , , , , , and 15 other author(s). CoRL, volume 229 of Proceedings of Machine Learning Research, page 3909-3928. PMLR, (2023)Zero-Shot Robotic Manipulation with Pre-Trained Image-Editing Diffusion Models., , , , , , and . ICLR, OpenReview.net, (2024)Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters., , , and . CoRR, (2024)Latent Conservative Objective Models for Data-Driven Crystal Structure Prediction., , , , , and . CoRR, (2023)Calibration of Encoder Decoder Models for Neural Machine Translation., and . CoRR, (2019)Diagnosing Bottlenecks in Deep Q-learning Algorithms., , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 2021-2030. PMLR, (2019)Conservative Q-Learning for Offline Reinforcement Learning., , , and . NeurIPS, (2020)