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cs.LG2026
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…
cs.LG2026
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…
cs.LG2026
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.…