3 papers
cs.AI2026
AdviSD: Learning to Advise Frontier LLMs via Targeted Multi-Turn Self-Distillation
Rishabh Agrawal, Hejie Cui, Shasha Li +2
A small trainable advisor can steer a frozen language-model executor using natural-language advice. In addition to learning from task rewards, the advisor can use feedback from com…
cs.LG2026
When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited
Rishabh Agrawal, Rahul Jain, Ashutosh Nayyar
Behavior Foundation Models (BFMs) enable scalable imitation learning (IL) by pretraining task-agnostic representations that can be rapidly adapted to new tasks. However, existing B…
cs.MA2026
Agents of Diffusion: Enhancing Diffusion Language Models with Multi-Agent Reinforcement Learning for Structured Data Generation (Extended Version)
Aja Khanal, Kaushik T. Ranade, Rishabh Agrawal +2
Generating high-quality structured data such as JSON records, remains a fundamental challenge for large language models (LLMs), particularly when semantic richness must coexist wit…