Honored to share that my paper "Foundation World Models for Agents that Learn, Verify, and Adapt Reliably Beyond Static Environments" received the Best Blue Sky Paper Award at #AAMAS2026! @aamasconf.bsky.social (1/n)

How can an agent improve by leveraging a learned world model without being misled by model errors? I wrote a blog post about our #ICLR2026 paper, Deep SPI, where I explain the main idea behind safe policy improvement in RL via world models. -> delgrange.me/post/deep_spi/

Deep SPI: Safe Policy Improvement via World Models | Florent Delgrange

A long-form explainer of Deep SPI: why ordinary on-policy auxiliary losses break after policy updates, how world models and neighborhood-constrained updates fix this, and what the resulting algorithm ...

delgrange.me

Glad to share that my paper, “Foundation World Models for Agents that Learn, Verify, and Adapt Reliably Beyond Static Environments” has been accepted at AAMAS 2026! 📄 Paper: arxiv.org/abs/2602.23997

📢 Deadline Extended! The submission deadline for the Adaptive and Learning Agents (ALA) Workshop at #AAMAS2026 (Paphos, Cyprus 🇨🇾) has been extended! 🗓️ Feb 26, 2026 alaworkshop2026.github.io

🎉 Excited to share that our paper, “Deep SPI: Safe Policy Improvement via World Models,” has been accepted to ICLR 2026 🇧🇷! 📄 full paper: arxiv.org/abs/2510.12312 🤝 with @raphael.avalos.fr and @willemropke.bsky.social 🧵

Really looking forward to ALA @ AAMAS 2026! Glad to be co-organizing this edition. If you’re working on adaptive & learning agents, I hope to see you in Paphos!

@ala-workshop.bsky.social · 9mo ago

Call for Papers | #ALA2026 @ #AAMAS2026 We’re excited to announce the 17th Adaptive & Learning Agents (ALA) Workshop at AAMAS 2026 in Paphos, Cyprus 🗓 Deadline: Feb 4, 2026 🌐 Details & submission: alaworkshop2026.github.io 🗓 Workshop: May 25–26, 2026

Mind the GAP! we've had a few works proposing techniques for enabling scaling in deep rl, such as MoEs, tokenization, & sparse training. ghada sokar and i looked further & found a bit more clarity into *what* enables scaling, leading us to simpler solutions (see GAP in figure)! 1/

🎓 PhD position available! Join our interdisciplinary research project on causal agent-based modelling! 🔍 Looking for curious minds with a MSc degree (or near to completing one) in CS/AI/related fields. 📍 Location: Utrecht University, NL 🗓️ Deadline: 16 June 2025 📩 Info: www.uu.nl/en/organisat...

PhD Position in Causal Agent-based Modelling of Complex Social Systems

Join this exciting interdisciplinary research project at the Centre for Complex Systems Studies and study causal agent-based modelling!

uu.nl

Happy to share our new paper (AAMAS 2025)! We combine reinforcement learning 🤖🧠 & reactive synthesis ⚙️ for learning scalable safe policies in complex tasks with formal guarantees. 📑paper: arxiv.org/abs/2402.13785 ✍️blogpost: delgrange.me/post/composi... A thread🧵⤵️

Composing Reinforcement Learning Policies, with Formal Guarantees | Florent Delgrange

Synthesizing controllers in large domains from verified world models and reinforcement learning policy composition.

delgrange.me

Still 7 days to submit your work to the ALA workshop at AAMAS! We welcome full papers, work in progress, and 2-page abstracts of recently published journal papers. All the info is available at ala-workshop.github.io.

Another must read for reinforcement learning. Answers many key questions for researchers; -Do I need multiple training runs? -How do I report model confidence? -And a great section on common mistakes to fend off reviewer 2 🧪 #DRL #reinforcementlearning #AI arxiv.org/abs/2304.01315

Empirical Design in Reinforcement Learning

Empirical design in reinforcement learning is no small task. Running good experiments requires attention to detail and at times significant computational resources. While compute resources available p...

arxiv.org

In addition to the Deep Learning Theory starter pack, I've also put together a starter pack for Reinforcement Learning Theory. Let me know if you'd like to be included or suggest someone to add to the list! go.bsky.app/LWyGAAu

If you're an RL researcher or RL adjacent, pipe up to make sure I've added you here! go.bsky.app/3WPHcHg