3 citations · 4 across the 7 of their papers we have counts for
8 papers
GRPO Training Dynamics for Small Language Models
Rajat Ghosh, Vaishnavi Bhargava, Henry Wong +2
Group Relative Policy Optimization (GRPO) has emerged as a memory-efficient reinforcement fine-tuning (RFT) technique for reasoning-intensive tasks. How- ever, GRPO training dynami…
Predictable GRPO: A Closed-Form Model of Training Dynamics
Rajat Ghosh, Datta Nimmaturi, Aryan Singhal +4
We develop a first-principles reduced-order model of these dynamics. Under a single mean-field assumption that summarizes the policy by its expected reward, we reduce the GRPO upda…
SWE-Tester: Training Open-Source LLMs for Issue Reproduction in Real-World Repositories
Aditya Bharat Soni, Rajat Ghosh, Vaishnavi Bhargava +2
Software testing is crucial for ensuring the correctness and reliability of software systems. Automated generation of issue reproduction tests from natural language issue descripti…
Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go
Yashshi Pipalani, Hritik Raj, Rajat Ghosh +2
Training data imbalance poses a major challenge for code LLMs. Most available data heavily over represents raw opensource code while underrepresenting broader software engineering…
BAR Conjecture: the Feasibility of Inference Budget-Constrained LLM Services with Authenticity and Reasoning
Jinan Zhou, Rajat Ghosh, Vaishnavi Bhargava +2
When designing LLM services, practitioners care about three key properties: inference-time budget, factual authenticity, and reasoning capacity. However, our analysis shows that no…
Predictive Scaling Laws for Efficient GRPO Training of Large Reasoning Models
Datta Nimmaturi, Vaishnavi Bhargava, Rajat Ghosh +2
Fine-tuning large language models (LLMs) for reasoning tasks using reinforcement learning methods like Group Relative Policy Optimization (GRPO) is computationally expensive. To ad…
CPP-UT-Bench: Can LLMs Write Complex Unit Tests in C++?
Vaishnavi Bhargava, Rajat Ghosh, Debojyoti Dutta
We introduce CPP-UT-Bench, a benchmark dataset to measure C++ unit test generation capability of a large language model (LLM). CPP-UT-Bench aims to reflect a broad and diverse set…
The ALCHEmist: Automated Labeling 500x CHEaper Than LLM Data Annotators
Tzu-Heng Huang, Catherine Cao, Vaishnavi Bhargava +1
Large pretrained models can be used as annotators, helping replace or augment crowdworkers and enabling distilling generalist models into smaller specialist models. Unfortunately,…