7 papers
Towards Understanding What State Space Models Learn About Code
Jiali Wu, Abhinav Anand, Shweta Verma +1
State Space Models (SSMs) have emerged as an efficient alternative to the Transformer architecture. Prior work shows that, when trained under comparable conditions, SSMs can match…
Efficient Post-training of LLMs for Code Generation With Offline Reinforcement Learning
Mingze Wu, Abhinav Anand, Shweta Verma +1
Post-training using online reinforcement learning (RL) is an important training step for LLMs, including code-generating models. However, online RL for code generation involves LLM…
Domain-Adaptable Reinforcement Learning for Code Generation with Dense Rewards
Erfan Aghadavoodi Jolfaei, Daniel Maninger, Abhinav Anand +2
Large language models show strong potential for automated code generation, but lack guarantees for correctness, quality, safety, and domain-specific constraints. For instance in ro…
Analysis of Long Range Dependency Understanding in State Space Models
Srividya Ravikumar, Abhinav Anand, Shweta Verma +1
Although state-space models (SSMs) have demonstrated strong performance on long-sequence benchmarks, most research has emphasized predictive accuracy rather than interpretability.…
CodeSSM: Towards State Space Models for Code Understanding
Shweta Verma, Abhinav Anand, Mira Mezini
Although transformers dominate many code-specific tasks, they have significant limitations. This paper explores State Space Models (SSMs) as a promising alternative for code unders…
Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs
Marina Sakharova, Abhinav Anand, Mira Mezini
Code-generating Large Language Models (LLMs) have become essential tools in modern software development, enhancing productivity and accelerating development. This paper aims to inv…