5 papers · 1 filter
Performance, Efficiency and Collapse -- Advantages and Challenges in Offline Post-training of Code LLMs
Abhinav Anand, Sanjana Reddy Pachika, Shweta Verma +1
Post-training with reinforcement learning (RL) is a critical phase in the development of code-generating large language models (LLMs), as it ensures adherence to instructions and t…
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…
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…
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.…