​Agenda

​​​​​🕒 5:30 pm – 6:00 pm
Welcome Reception and Opening Remarks

​🎤 Marijan Cipcic, Principal Events Manager at Daytona

​🕒 6:00 pm – 6:15 pm
Talk "Today's Agents Don't Live In Episodes"

​🎤 Muhammad Annas Hashmi, DevRel at Daytona

​​​​​​​Outline:

​The 'episode' (short, stateless, resettable) has been RL's foundational abstraction since ATARI. It underpins the Gym API, GRPO, PPO, and the conventional sandbox lifecycle. Today's agents no longer fit it. Tasks span for days; the env state at hour 18 of an agent session with warm caches, installed deps, live processes, open sockets, dirty git tree, is worth hours of wall clock to reproduce.

Three things are scaling simultaneously. Rollout horizon: seconds -> days. Env state: disposable between episodes -> first-class learning substrate. Branching: absent in modern LLM-RL -> speculative fork trees. Each stresses the inherited toolkit in a different way, and all three have been gated on the same missing primitives: VMs you can fork cheaply, pause without killing processes, snapshot mid-run, and resume hours later.

This talk walks through what opens up when those primitives become available. Live demo of long-horizon sessionful rollouts, mid-trajectory forking, and cross-calendar-time training. The research questions that follow (long-horizon benchmarks, speculative RL algorithms, event-driven training, to name a few) are where the next wave of agent RL gets built.

​🕒 6:15 pm – 6:30 pm
Talk "TBA"

​🎤 Speaker TBA

​​​​​​​Outline:

​TBA

​🕒 6:30 pm – 6:45 pm
Talk "TBA"

​🎤 Speaker TBA

​​​​​​​Outline:

​TBA

​🕒 6:45 pm – 7:00 pm
Talk "TBA"

​🎤 Speaker TBA

​​​​​​​Outline:

​TBA

​🕒 7:00 pm – 7:10 pm
Talk "TBA"

​🎤 Speaker TBA

​​​​​​​Outline:

​TBA

​​​​​​​​​​🕒 7:10 pm - 8:30 pm

​​Networking

​With food and beverages

​​About event

​An engaging meetup designed for AI researchers to connect, share ideas, and explore the latest advancements in artificial intelligence. The event features informal networking, short talks, and discussions on current research trends, fostering collaboration and knowledge exchange within the AI community.