2 citations · 4 across the 13 of their papers we have counts for
5 papers · 1 filter
Iterative Policy Optimization: Batch-Online Multi-Turn RL via Single-Turn RL
Daniel R. Jiang, Ankur Samanta, Yukai Yang +3
Practical LLM agents often operate over multi-turn conversations where success is determined only after the full interaction ends. Most multi-turn RL methods train via on-policy ro…
Imbalanced Gradients in RL Post-Training of Multi-Task LLMs
Runzhe Wu, Ankur Samanta, Ayush Jain +7
Multi-task post-training of large language models (LLMs) is typically performed by mixing datasets from different tasks and optimizing them jointly. This approach implicitly assume…
Self-Improvement of Language Models by Post-Training on Multi-Agent Debate
Ankur Samanta, Akshayaa Magesh, Runzhe Wu +7
Self-improvement, where models improve beyond their current performance without external supervision, remains a challenge. The core difficulty is sourcing a training signal stronge…
A Novel Real-Time Full-Color 3D Holographic (Diffractive) Video Capture, Processing, and Transmission Pipeline Using Off-The-Shelf Hardware
Ankur Samanta, Gregor Mackenzie, Tyler Rathkamp +4
This paper details the world's first live 3D holographic (diffractive) video call using off-the-shelf hardware. We introduce a novel pipeline that facilitates the capture, processi…
FragmentNet: Adaptive Graph Fragmentation for Graph-to-Sequence Molecular Representation Learning
Ankur Samanta, Rohan Gupta, Aditi Misra +2
Molecular representation learning methods typically tokenize molecules as individual atoms or use rigid, rule-based fragment decompositions, limiting their ability to capture meani…