Papers by Vignesh Kothapalli
Distilling the Essence: Efficient Reasoning Distillation via Sequence Truncation (2026.findings-acl)
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Wei-Rui Chen, Vignesh Kothapalli, Ata Fatahibaarzi, Hejian Sang, Shao Tang, Qingquan Song, Zhipeng Wang, Muhammad Abdul-Mageed
| Challenge: | a large reasoning model (LRM) training on large amounts of reasoning data is computationally expensive. |
| Approach: | They propose a method to quantify computation-quality tradeoffs as a function of sequence length. |
| Outcome: | The proposed method reduces training time, memory and FLOPs by 50% on long training sequences while retaining the full-sequence performance. |
Scaling Down, Serving Fast: Compressing and Deploying Efficient LLMs for Recommendation Systems (2025.emnlp-industry)
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Kayhan Behdin, Ata Fatahibaarzi, Qingquan Song, Yun Dai, Aman Gupta, Zhipeng Wang, Hejian Sang, Shao Tang, Gregory Dexter, Sirou Zhu, Siyu Zhu, Tejas Dharamsi, Vignesh Kothapalli, Zhoutong Fu, Yihan Cao, Pin-Lun Hsu, Fedor Borisyuk, Natesh S. Pillai, Luke Simon, Rahul Mazumder
| Challenge: | Large language models (LLMs) have demonstrated remarkable performance across a wide range of industrial applications. |
| Approach: | They propose two techniques for training and deploying small language models that deliver high performance for a variety of industry use cases. |
| Outcome: | The proposed techniques retain much of the quality of larger models while reducing training/serving costs and latency. |
CoT-ICL Lab: A Synthetic Framework for Studying Chain-of-Thought Learning from In-Context Demonstrations (2025.acl-long)
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| Challenge: | In-context learning and CoT are still poorly understood, but the precise mechanisms and architectural factors driving ICL and Co T are still unclear. |
| Approach: | They propose a framework and methodology to generate synthetic tokenized datasets and study chain-of-thought (CoT) in-context learning in language models. |
| Outcome: | The proposed framework and methodology allows fine grained control over the complexity of in-context examples by decoupling causal structure from underlying token processing functions. |
To Think or Not to Think: The Hidden Cost of Meta-Training with Excessive CoT Examples (2026.acl-long)
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| Challenge: | Chain-of-thought (CoT) prompting and in-context learning (ICL) have unlocked significant reasoning capabilities in large language models (LLMs). |
| Approach: | They propose a meta-training technique to learn reasoning tasks in-context using CoT examples. |
| Outcome: | The proposed methods improve performance on novel reasoning tasks even when there are no CoT examples available in-context. |