5 papers
Learning to Detect UI Principle Violations via Reinforcement Learning
Nishi Mehta, Swathi Alse, Himani Kumavat +3
Small language models and coding agents increasingly generate web front-end code, yet their outputs are typically evaluated primarily for functional correctness. A generated interf…
Translate-R1: Cost-Aware Translation Tool Use via Reinforcement Learning
Pratik Jayarao, Chaitanya Dwivedi, Himanshu Gupta +5
The performance gap across languages in LLMs is well documented, and closing it natively requires pretraining or fine-tuning on corpora that, for most languages, are quite limited.…
Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts
Chaitanya Dwivedi, Binxuan Huang, Himanshu Gupta +3
Mixture-of-Experts (MoE) has become the dominant architecture for scaling large language models: frontier models routinely decouple total parameters from per-token computation thro…
Code Mixologist : A Practitioner's Guide to Building Code-Mixed LLMs
Himanshu Gupta, Pratik Jayarao, Chaitanya Dwivedi +1
Code-mixing and code-switching (CSW) remain challenging phenomena for large language models (LLMs). Despite recent advances in multilingual modeling, LLMs often struggle in mixed-l…
Explicit Reasoning Makes Better Judges: A Systematic Study on Accuracy, Efficiency, and Robustness
Pratik Jayarao, Himanshu Gupta, Neeraj Varshney +1
As Large Language Models (LLMs) are increasingly adopted as automated judges in benchmarking and reward modeling, ensuring their reliability, efficiency, and robustness has become…