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Rachit Bansal

Rachit Bansal has 3 papers in our library, in 2022–2025, most of them on Language models & pretraining, Evaluation & benchmarks; often with Bhuwan Dhingra, Danish Pruthi, David Brandfonbrener.

3 papers in our library · 2022–2025

OpenAlex · h-index 7 · 21 works · 278 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 123 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7243

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Harvard UniversityCambridge · United States2026 · 1 papercurrent🎓Delhi Technological UniversityNew Delhi · India2021–2022 · 12 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.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 works

🧭 Topics 2Research subjects and languages the work is about.Evaluation & benchmarks · 1 workLanguage models & pretraining · 2 works

 Works by year

2020s

20251 paper20241 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Evaluating Explanations: How Much Do Explanations from the Teacher Aid Students? (2022 · 113 citations)
    Feature-attribution methods that mark certain input tokens as important are widely used to explain NLP models.
  2. LLM Augmented LLMs: Expanding Capabilities through Composition (2024 · 30 citations)
    Large foundation models are expensive to extend with new skills, yet many specialized models already exist.
  3. The Art of Scaling Reinforcement Learning Compute for LLMs (2025 · 20 citations)
    Reinforcement learning now consumes a large share of LLM training compute. Unlike pre-training, though, it has no dependable way to forecast how a training recipe will behave as budgets grow. The authors tackle this with a study totalling over 400,000 GPU-hours on Nvidia GB200 hardware. Most runs use an 8B dense…

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Evaluating Explanations: How Much Do Explanations from the Teacher Aid Students?Transactions of the Association for Computational Linguistics (TACL)Danish Pruthi, Rachit Bansal, Bhuwan Dhingra, et al.2022 · 113 citations
  2. Linear Connectivity Reveals Generalization Strategies ↗arXiv (Cornell University) · no summary yetJeevesh Juneja et al.2022 · 32 citations
  3. LLM Augmented LLMs: Expanding Capabilities through CompositionarXiv:2401.02412Rachit Bansal, Bidisha Samanta, Siddharth Dalmia, et al.2024 · 30 citations
  4. The Art of Scaling Reinforcement Learning Compute for LLMsarXiv:2510.13786Devvrit Khatri, Lovish Madaan, Rishabh Tiwari, et al.2025 · 20 citations

 Co-authors here

12 of 15 co-authors with a page, the most papers together first.

Venues

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

 Cited by

Papers of our library cite their works 123 times (of the 149,439 papers whose bibliographies we read); 23 citing papers with a page here →.

Updated

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