Maitreyi Chatterjee

Guest Author

Maitreyi Chatterjee is a software engineer at a big tech company in the bay area, where she works on large-scale privacy and compliance infrastructure for production AI systems. She is also an active AI researcher with work spanning natural language processing, reinforcement learning, and AI systems. Her interests focus on building reliable, accountable, and evaluation-driven AI at scale, and she regularly analyzes emerging trends in model architectures, training dynamics, and real-world deployment.

RLVR DDM

Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025)

Every year, NeurIPS produces hundreds of impressive papers, and a handful that subtly reset how practitioners think about scaling, evaluation and system design. In 2025, the most consequential works weren't about a single breakthrough model. Instead, they challenged fundamental assumptions that academicians and corporations have quietly relied on: Bigger models mean better reasoning, RL creates new capabilities, attention is “solved” and generative models inevitably memorize.

Maitreyi Chatterjee,Devansh Agarwal