---
title: "Chelsea Finn — all works — Elamur Research"
description: "All 51 works of Chelsea Finn in the Elamur Research library, the most cited first: our summaries where we have them, else the original."
canonical: "https://research.elamur.ai/free/en/authors/chelsea-finn/works/"
---
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# Chelsea Finn — all works

51 works · [← Chelsea Finn](https://md.research.elamur.ai/free/en/authors/chelsea-finn/)

1. [**Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks arXiv:1703.03400**Chelsea Finn, Pieter Abbeel, Sergey Levine*2017 · 6,549 citations*](https://md.research.elamur.ai/free/en/papers/finn-2017-model-agnostic-meta-learning-fast/)
2. [**Direct Preference Optimization: Your Language Model is Secretly a Reward Model arXiv:2305.18290**Rafael Rafailov, Archit Sharma, Eric Mitchell, et al.*2023 · 2,896 citations*](https://md.research.elamur.ai/free/en/papers/rafailov-2023-direct-preference-optimization-language-model/)
3. **[End-to-End Training of Deep Visuomotor Policies ↗](https://arxiv.org/abs/1504.00702) arXiv:1504.00702 · no summary yet**Sergey Levine et al.*2015 · 1,463 citations*
4. [**Do As I Can, Not As I Say: Grounding Language in Robotic Affordances arXiv:2204.01691**Michael Ahn, Anthony Brohan, Noah Brown, et al.*2022 · 941 citations*](https://md.research.elamur.ai/free/en/papers/ahn-2022-i-i-say-grounding-language/)
5. [**DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature arXiv:2301.11305**Eric Mitchell, Yoonho Lee, Alexander Khazatsky, et al.*2023 · 481 citations*](https://md.research.elamur.ai/free/en/papers/mitchell-2023-detectgpt-zero-shot-machine-generated/)
6. **[Deep Spatial Autoencoders for Visuomotor Learning ↗](https://arxiv.org/abs/1509.06113) arXiv:1509.06113 · no summary yet**Chelsea Finn et al.*2015 · 461 citations*
7. **[Model-Based Reinforcement Learning for Atari ↗](https://arxiv.org/abs/1903.00374) arXiv (Cornell University) · no summary yet**Łukasz Kaiser et al.*2019 · 429 citations*
8. **[WILDS: A Benchmark of in-the-Wild Distribution Shifts ↗](https://arxiv.org/abs/2012.07421) CaltechAUTHORS (California Institute of Technology) · no summary yet**Pang Wei Koh et al.*2020 · 405 citations*
9. **[Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback ↗](https://aclanthology.org/2023.emnlp-main.330/) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · no summary yet**Katherine Tian et al.*2023 · 393 citations*
10. **[Fast Model Editing at Scale ↗](https://arxiv.org/abs/2110.11309) arXiv:2110.11309 · no summary yet**Eric Mitchell et al.*2021 · 332 citations*
11. **[MOPO: Model-based Offline Policy Optimization ↗](https://arxiv.org/abs/2005.13239) arXiv (Cornell University) · no summary yet**Tianhe Yu et al.*2020 · 260 citations*
12. **[Memory-Based Model Editing at Scale ↗](https://arxiv.org/abs/2206.06520) arXiv:2206.06520 · no summary yet**Eric Mitchell et al.*2022 · 255 citations*
13. **[OpenVLA: An Open-Source Vision-Language-Action Model ↗](https://arxiv.org/abs/2406.09246) arXiv:2406.09246 · no summary yet**Moo Jin Kim et al.*2024 · 254 citations*
14. [**Gradient Surgery for Multi-Task Learning arXiv:2001.06782**Tianhe Yu, Saurabh Kumar, Abhishek Gupta, et al.*2020 · 233 citations*](https://md.research.elamur.ai/free/en/papers/yu-2020-gradient-surgery-multi-task-learning/)
15. **[Open X-Embodiment: Robotic Learning Datasets and RT-X Models ↗](https://arxiv.org/abs/2310.08864) arXiv:2310.08864 · no summary yet**Embodiment Collaboration et al.*2023 · 204 citations*
16. **[Recasting Gradient-Based Meta-Learning as Hierarchical Bayes ↗](https://arxiv.org/abs/1801.08930) arXiv (Cornell University) · no summary yet**Erin Grant et al.*2018 · 165 citations*
17. [**Analyzing and Mitigating Object Hallucination in Large Vision-Language Models arXiv:2310.00754**Yiyang Zhou, Chenhang Cui, Jaehong Yoon, et al.*2023 · 134 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-analyzing-mitigating-object-hallucination-large/)
18. **[Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success ↗](https://doi.org/10.15607/rss.2025.xxi.017) no summary yet**Moo Jin Kim, Chelsea Finn, Percy Liang*2025 · 132 citations*
19. [**Fine-tuning Language Models for Factuality arXiv:2311.08401**Katherine Tian, Eric Mitchell, Huaxiu Yao, et al.*2023 · 127 citations*](https://md.research.elamur.ai/free/en/papers/tian-2023-fine-tuning-language-models-factuality/)
20. **[Just Train Twice: Improving Group Robustness without Training Group Information ↗](https://arxiv.org/abs/2107.09044) arXiv (Cornell University) · no summary yet**Evan Zheran Liu et al.*2021 · 125 citations*
21. **[Adapting Deep Visuomotor Representations with Weak Pairwise Constraints ↗](https://doi.org/10.1007/978-3-030-43089-4_44) Springer proceedings in advanced robotics · no summary yet**Eric Tzeng et al.*2015 · 124 citations*
22. [**FAST: Efficient Action Tokenization for Vision-Language-Action Models arXiv:2501.09747**Karl Pertsch, Kyle Stachowicz, Brian Ichter, et al.*2025 · 123 citations*](https://md.research.elamur.ai/free/en/papers/pertsch-2025-fast-efficient-action-tokenization-vision/)
23. **[Disentangling Length from Quality in Direct Preference Optimization](https://md.research.elamur.ai/free/en/papers/park-2024-disentangling-length-quality-direct-preference/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ryan Park, Rafael Rafailov, Stefano Ermon, et al.*2024 · 105 citations*
24. **[Language-Driven Representation Learning for Robotics ↗](https://doi.org/10.15607/rss.2023.xix.032) arXiv:2302.12766 · no summary yet**Siddharth Karamcheti et al.*2023 · 88 citations*
25. [**From r to Q*: Your Language Model is Secretly a Q-Function arXiv:2404.12358**Rafael Rafailov, Joey Hejna, Ryan Park, et al.*2024 · 72 citations*](https://md.research.elamur.ai/free/en/papers/rafailov-2024-r-q-language-model-secretly/)
26. [**Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents arXiv:2408.07199**Pranav Putta, Edmund Mills, Naman Garg, et al.*2024 · 68 citations*](https://md.research.elamur.ai/free/en/papers/putta-2024-agent-q-advanced-reasoning-learning/)
27. **[Surgical Fine-Tuning Improves Adaptation to Distribution Shifts ↗](https://arxiv.org/abs/2210.11466) arXiv (Cornell University) · no summary yet**Yoonho Lee et al.*2022 · 67 citations*
28. **[Generative Reward Models ↗](https://arxiv.org/abs/2410.12832) arXiv:2410.12832 · no summary yet**Dakota Mahan et al.*2024 · 58 citations*
29. **[Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data ↗](https://arxiv.org/abs/2404.14367) arXiv (Cornell University) · no summary yet**Fahim Tajwar et al.*2024 · 54 citations*
30. **[Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference ↗](https://aclanthology.org/2022.emnlp-main.115/) Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · no summary yet**Eric Mitchell et al.*2022 · 53 citations*
31. **[Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought ↗](https://arxiv.org/abs/2501.04682) arXiv:2501.04682 · no summary yet**Violet Xiang et al.*2025 · 47 citations*
32. [**An Emulator for Fine-Tuning Large Language Models using Small Language Models arXiv:2310.12962**Eric Mitchell, Rafael Rafailov, Archit Sharma, et al.*2023 · 36 citations*](https://md.research.elamur.ai/free/en/papers/mitchell-2023-emulator-fine-tuning-large-language/)
33. **[Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models ↗](https://doi.org/10.1145/3600211.3604690) Proceedings of the AAAI/ACM Conference on AI Ethics and Society · no summary yet**Peter B. Henderson et al.*2023 · 36 citations*
34. **[Meta-Inverse Reinforcement Learning with Probabilistic Context Variables ↗](https://arxiv.org/abs/1909.09314) arXiv (Cornell University) · no summary yet**Lantao Yu et al.*2019 · 30 citations*
35. **[Meta-Learning Online Adaptation of Language Models ↗](https://arxiv.org/abs/2305.15076) arXiv:2305.15076 · no summary yet**Nathan Hu et al.*2023 · 22 citations*
36. **[PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs ↗](https://arxiv.org/abs/2402.07872) arXiv (Cornell University) · no summary yet**Soroush Nasiriany et al.*2024 · 21 citations*
37. **[Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning ↗](https://arxiv.org/abs/2601.16163) arXiv (Cornell University) · no summary yet**Moo Jin Kim et al.*2026 · 19 citations*
38. **[Extending the WILDS Benchmark for Unsupervised Adaptation ↗](https://arxiv.org/abs/2112.05090) arXiv (Cornell University) · no summary yet**Shiori Sagawa et al.*2021 · 18 citations*
39. **[Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices ↗](https://arxiv.org/abs/2008.02790) arXiv (Cornell University) · no summary yet**Evan Zheran Liu et al.*2020 · 14 citations*
40. **[Weakly-Supervised Reinforcement Learning for Controllable Behavior ↗](https://arxiv.org/abs/2004.02860) arXiv (Cornell University) · no summary yet**Lisa Lee et al.*2020 · 7 citations*
41. **[RoboReward: General-Purpose Vision-Language Reward Models for Robotics ↗](https://arxiv.org/abs/2601.00675) arXiv (Cornell University) · no summary yet**Tony Lee et al.*2026 · 6 citations*
42. **[LLM-as-a-Verifier: A General-Purpose Verification Framework ↗](https://arxiv.org/abs/2607.05391) arXiv (Cornell University) · no summary yet**Jacky Kwok et al.*2026 · 5 citations*
43. **[FSPO: Few-Shot Optimization of Synthetic Preferences Personalizes to Real Users ↗](https://arxiv.org/abs/2502.19312) arXiv (Cornell University) · no summary yet**Anikait Singh et al.*2025 · 5 citations*
44. **[MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance ↗](https://arxiv.org/abs/2112.03575) arXiv (Cornell University) · no summary yet**Michael Luo et al.*2021 · 4 citations*
45. **[RLAD: Training LLMs to Discover Abstractions for Solving Reasoning Problems ↗](https://arxiv.org/abs/2510.02263) arXiv (Cornell University) · no summary yet**Qu, Yuxiao et al.*2025 · 3 citations*
46. **[Challenges of Acquiring Compositional Inductive Biases via Meta-Learning ↗](https://openalex.org/W3205009531) National Conference on Artificial Intelligence · no summary yet**Eric Mitchell, Chelsea Finn, Christopher D. Manning*2021 · 3 citations*
47. **[Learning Compact Convolutional Neural Networks with Nested Dropout ↗](https://arxiv.org/abs/1412.7155) arXiv (Cornell University) · no summary yet**Chelsea Finn, Lisa Anne Hendricks, Trevor Jackson Darrell*2014 · 3 citations*
48. **[VLAW: Iterative Co-Improvement of Vision-Language-Action Policy and World Model ↗](https://arxiv.org/abs/2602.12063) arXiv (Cornell University) · no summary yet**Yanjiang Guo et al.*2026*
49. **[ScholarCatalyst: A Benchmark for Retrieving Papers That Inspire New Research ↗](https://arxiv.org/abs/2610.02202) arXiv (Cornell University) · no summary yet**Sohyeon Kim et al.*2026*
50. **[Contrastive Example-Based Control ↗](https://arxiv.org/abs/2307.13101) arXiv (Cornell University) · no summary yet**Kyle Hatch et al.*2023*
51. **[End-toend training of deep visuomotor policies. arXiv preprint arXiv:1504.00702 ↗](https://openalex.org/W7095372322) no summary yet**Sergey Levine et al.*2015*