🧑‍🔬 Archit Sharma

🏛️ Affiliations

🎓Stanford UniversityStanford · United States2021–2025 · 19 papers🏢GoogleMountain View · United States2019–2025 · 6 papers🏢Google DeepMindLondon · United Kingdom2024–2025 · 2 papers🎓Harvard UniversityCambridge · United States2023 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Direct Preference Optimization: Your Language Model is Secretly a Reward Model (2023 · 2,896 citations)
   Making a pretrained language model behave the way people prefer usually relies on RLHF. A reward model is first fitted to pairwise human judgments under the Bradley-Terry model. The policy is then tuned with PPO to maximise that reward, while a KL penalty keeps it near the SFT reference. This pipeline needs several…
2. An Emulator for Fine-Tuning Large Language Models using Small Language Models (2023 · 36 citations)
   Instruction-tuned LLMs come from two stages: broad pre-training and narrower fine-tuning. It is often assumed that pre-training supplies knowledge while fine-tuning mainly shapes behavior, but this has rarely been tested directly. The authors propose emulated fine-tuning (EFT), which treats a fine-tuned model as the…

All works

8 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

1. Direct Preference Optimization: Your Language Model is Secretly a Reward ModelarXiv:2305.18290Rafael Rafailov, Archit Sharma, Eric Mitchell, et al.2023 · 2,896 citations
2. Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback ↗Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · no summary yetKatherine Tian et al.2023 · 393 citations
3. Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration 0 ↗Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · no summary yetAbby O'Neill et al.2024 · 231 citations
4. Open X-Embodiment: Robotic Learning Datasets and RT-X Models ↗arXiv:2310.08864 · no summary yetEmbodiment Collaboration et al.2023 · 204 citations
5. Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data ↗arXiv (Cornell University) · no summary yetFahim Tajwar et al.2024 · 54 citations
6. Stream of Search (SoS): Learning to Search in Language ↗arXiv:2404.03683 · no summary yetKanishk Gandhi et al.2024 · 49 citations
7. An Emulator for Fine-Tuning Large Language Models using Small Language ModelsarXiv:2310.12962Eric Mitchell, Rafael Rafailov, Archit Sharma, et al.2023 · 36 citations
8. FSPO: Few-Shot Optimization of Synthetic Preferences Personalizes to Real Users ↗arXiv (Cornell University) · no summary yetAnikait Singh et al.2025 · 5 citations

🤝 Co-authors here

5 co-authors with a page, the most papers together first.

• Chelsea Finn ×2
• Christopher D. Manning ×2
• Eric Mitchell ×2
• Rafael Rafailov ×2
• Stefano Ermon

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Proceedings - IEEE International Conference on Robotics and Automation/Proceedings
• Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

🔗 Cited by

Papers of our library cite their works 3,170 times (of the 149,439 papers whose bibliographies we read); 641 citing papers with a page here →.

Updated 30 October 2026

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