12 papers
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…
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…
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…
CR-Bench: Evaluating the Real-World Utility of AI Code Review Agents
Kristen Pereira, Neelabh Sinha, Rajat Ghosh +1
Recent advances in frontier large language models have enabled code review agents that operate in open-ended, reasoning-intensive settings. However, the lack of standardized benchm…
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…
Action Shapley: A Training Data Selection Metric for World Model in Reinforcement Learning
Rajat Ghosh, Debojyoti Dutta
Numerous offline and model-based reinforcement learning systems incorporate world models to emulate the inherent environments. A world model is particularly important in scenarios…