Join Workato's AI Journal Club series—we're bringing together the best AI researchers to share papers and exchange perspectives on how AI research is shaping real-world systems.
Who Should Attend
AI Researchers and practitioners working at the intersection of AI research and real-world systems.
Schedule
5:30–6:00 PM: Check-in and registration
6:00–6:15 PM: Welcome to Workato
6:15–6:45 PM: Talk by Allen Chuang (Google)
6:45–7:00 PM: Q&A
7:00–7:30 PM: Talk by Ray Liu (NVIDIA)
7:30–7:45 PM: Q&A
7:45-8:30 PM: Networking
Please arrive by 6:00 PM. We politely ask that attendees arrive by this time out of respect for our speaker.
Sessions
Allen Chuang — Taming the Dynamics of LLM Reasoning: Test-Time Exploration, Data Scheduling, and On-Policy Distillation
Scaling reasoning capabilities in Large Language Models (LLMs) requires navigating complex optimization and search dynamics across both inference and post-training. However, modern reasoning pipelines suffer from critical inefficiencies throughout the model lifecycle: test-time decoding wastes compute on redundant search trajectories, reinforcement learning (RL) struggles with uniform data pacing, and on-policy distillation frequently undergoes catastrophic truncation collapse driven by length inflation. In this talk, I will present a unified perspective on understanding and taming reasoning dynamics. First, we examine inference-time search, demonstrating how Decoding Tree Sketching (DTS) enables structured exploration and early termination at decision tokens to eliminate redundant rollouts without retraining. Next, we turn to post-training optimization, showing how Adaptive Data Scheduling (ADS) leverages semantic clustering and policy-boundary selection to dynamically pace LLM RL curricula. Finally, we analyze the failure modes of On-Policy Distillation and introduce StableOPD to suppress trajectory explosion and restore distillation stability. Together, these methods provide practical mechanisms for building effici