ReGal: A First Look at PPO-based Legal AI for Judgment Prediction and Summarization in India

Emversity, an Indian startup focused on job-ready training, has successfully raised $30 million in a new funding round, doubling its valuation as it aims to scale its operations in a market increasingly focused on skills that artificial intelligence cannot replace.

Researchers at the University of Surrey have developed the world's first dataset designed to enhance English-to-Malayalam machine translation, addressing a significant gap for this low-resource language spoken by over 38 million people in India.

A recent study has introduced a framework aimed at mitigating hallucination issues in Multimodal Large Language Models (MLLMs) during Reinforcement Learning (RL) optimization. The research identifies key factors contributing to hallucinations, including over-reliance on visual reasoning and insufficient exploration diversity. The proposed framework incorporates modules for caption feedback, diversity-aware sampling, and conflict regularization to enhance model reliability.

A new dataset named IndRegBias has been introduced to study regional biases in English and code-mixed comments on social media platforms like Reddit and YouTube, focusing on Indian contexts. This dataset comprises 25,000 comments that reflect regional biases, which have been less explored compared to other social biases such as gender and race.

A new study presents an Edge-AI perception node designed for real-time traffic violation analytics and safety event dissemination in India, addressing the challenges posed by rapid motorization and a significant enforcement gap, with over 11 million violations recorded in 2023.

A recent study has revealed that the group-relative advantage estimator used in Reinforcement Learning from Verifier Rewards (RLVR) is biased, systematically underestimating advantages for difficult prompts while overestimating them for easier ones. This imbalance can lead to ineffective exploration and exploitation strategies in training large language models.

A recent study has highlighted the limitations of traditional reinforcement learning (RL) architectures in non-ergodic environments, where long-term outcomes depend on specific trajectories rather than ensemble averages. This research extends previous findings, demonstrating that deep RL implementations also yield suboptimal policies under these conditions.

A recent study introduces Uniqueness-Aware Reinforcement Learning (UARL), a novel approach aimed at enhancing the problem-solving capabilities of large language models (LLMs) by rewarding rare and effective solution strategies. This method addresses the common issue of exploration collapse in reinforcement learning, where models tend to converge on a limited set of reasoning patterns, thereby stifling diversity in solutions.