5 citations · 9 across the 18 of their papers we have counts for
7 papers · 2 filters
The Art of Scaling Test-Time Compute for Large Language Models
Aradhye Agarwal, Ayan Sengupta, Tanmoy Chakraborty
Test-time scaling (TTS) -- the dynamic allocation of compute during inference -- is a promising direction for improving reasoning in large language models (LLMs). However, a system…
Value-Guided KV Compression for LLMs via Approximated CUR Decomposition
Ayan Sengupta, Siddhant Chaudhary, Tanmoy Chakraborty
Key-value (KV) cache compression has emerged as a critical technique for reducing the memory and latency overhead of autoregressive language models during inference. Prior approach…
Position: Enough of Scaling LLMs! Lets Focus on Downscaling
Yash Goel, Ayan Sengupta, Tanmoy Chakraborty
We challenge the dominant focus on neural scaling laws and advocate for a paradigm shift toward downscaling in the development of large language models (LLMs). While scaling laws h…
First Finish Search: Efficient Test-Time Scaling in Large Language Models
Aradhye Agarwal, Ayan Sengupta, Tanmoy Chakraborty
Test-time scaling (TTS), which involves dynamic allocation of compute during inference, offers a promising way to improve reasoning in large language models. While existing TTS met…
On the Generalization vs Fidelity Paradox in Knowledge Distillation
Suhas Kamasetty Ramesh, Ayan Sengupta, Tanmoy Chakraborty
Knowledge distillation (KD) is a key technique for compressing large language models into smaller ones while preserving performance. Despite the recent traction of KD research, its…
You Only Prune Once: Designing Calibration-Free Model Compression With Policy Learning
Ayan Sengupta, Siddhant Chaudhary, Tanmoy Chakraborty
The ever-increasing size of large language models (LLMs) presents significant challenges for deployment due to their heavy computational and memory requirements. Current model prun…
How to Upscale Neural Networks with Scaling Law? A Survey and Practical Guidelines
Ayan Sengupta, Yash Goel, Tanmoy Chakraborty
Neural scaling laws have revolutionized the design and optimization of large-scale AI models by revealing predictable relationships between model size, dataset volume, and computat…