Meta learning and recursive self-improvement are old ideas. Foundation models breathe new life into them. Our new survey, “Self-Improvements in Modern Agentic Systems,” reviews how the concepts are continuing to evolve. Paper: arxiv.org/abs/2607.13104 Project: selfimproving-agent.github.i… Github: github.com/selfimproving-age…

Excited to share our new survey! As Juergen noted, foundation models have breathed new life into the long-standing idea of self-improvement. But how has this happened, and what makes this new wave different? Our survey seeks to answer these questions by developing a foundational understanding of self-improvement, tracing how the field has evolved and intersected with related research areas, and providing a comprehensive overview of current technologies and research. We warmly welcome your suggestions, feedback, and discussion!

For long horizon tasks in LLM, PPO and GRPO are no longer suitable. Scaling off-policy algorithms will the next bet.

Qwen is nothing without its people. ❤️🥃

# Made a small contribution Putting in some extra hours on Chinese New Year's Eve – finally ready to enjoy the holidays!

🚀 Introducing Qwen3-Max-Thinking, our most capable reasoning model yet. Trained with massive scale and advanced RL, it delivers strong performance across reasoning, knowledge, tool use, and agent capabilities. ✨ Key innovations: ✅ Adaptive tool-use: intelligently leverages Search, Memory & Code Interpreter without manual selection ✅ Test-time scaling: multi-round self-reflection beats Gemini 3 Pro on reasoning ✅ From complex math (98.0 on HMMT Feb) to agentic search (49.8 on HLE)—it just thinks better. 🧠 Think deeper. Solve harder. Try the adaptive reasoning experience now: chat.qwen.ai/ Completions API:  modelstudio.console.alibabac… Responses API:  alibabacloud.com/help/en/mod… blog:  qwen.ai/blog?id=qwen3-max-th…

📢 Just 8 weeks until #CoLLAs2025! Modern ML thrives in benchmarks but struggles in the wild. CoLLAs is where we tackle non-stationarity head-on: catastrophic forgetting, distribution shift, continual RL, online adaptation, and lifelong learning for ML. 📍 Aug 11–14 @Penn 🧠 Keynotes, Workshops, tutorials, posters & community 🔗 lifelong-ml.cc/Conferences/2… #ContinualLearning #LifelongLearning #AI #MachineLearning

🚀RL algorithms are shaping the post-training of LLMs, but how do their objectives connect? In this blog, I explore their relationships and provide a unified perspective through the Policy Gradient Theorem—the backbone of policy gradient methods. Dive in: lancelqf.github.io/note/llm_…

BREAKING: Amii Chief Scientific Advisor, Richard S. Sutton, has been awarded the A.M. Turing Award, the highest honour in computer science, alongside Andrew Barto! Read the official @TheOfficialACM announcement: hubs.la/Q039nZXM0 #TuringAward #AI #ReinforcementLearning

🚨 Call for Reviewers! 🚨 Want to contribute to advancing lifelong learning research? #CoLLAs2025 is looking for expert reviewers! 📝 Help shape the field by reviewing cutting-edge research in continual and lifelong learning. 🔗 Apply here: forms.gle/z1VFR4FRCbHjDvSa6 #MachineLearning #LifelongLearning #ContinualLearning #AI #AcademicReview

Would you believe that deep RL can work without replay buffers, target networks, or batch updates? Our recent work gets deep RL agents to learn from a continuous stream of data one sample at a time without storing any sample. Joint work with @Gautham529 and @rupammahmood.

Our NeurIPS paper is now on arXiv: We introduce Action Value Gradient (AVG), a novel incremental deep RL method that learns in real-time, one sample at a time — no batch updates, target networks or a replay buffer! Co-authors @mhmd_elsaye @bellingerc @white_martha @rupammahmood

RLC will be held at the Univ. of Alberta, Edmonton, in 2025. I'm happy to say that we now have the conference's website out: rl-conference.cc/index.html We'll continue to update it, and the CFP will be out soon, but the relevant dates are already there. @RL_Conference @UAlberta

📢 Exciting News! The Fourth Conference on Lifelong Learning Agents (CoLLAs 2025) will be held at the University of Pennsylvania (@Penn) in Philadelphia, USA 🇺🇸 🗓️ Important Dates: Abstract Deadline: Feb 21, 2025 Submission Deadline: Feb 26, 2025 Conference Dates: Aug 11 - Aug 14, 2025 We invite submissions that present new theories, methodologies, applications, or insights into algorithms and benchmarks designed for non-i.i.d. and non-stationary settings. Accepted papers will be published in the Proceedings of Machine Learning Research (PMLR). 📚 Full CFP: lifelong-ml.cc/Conferences/2… #CoLLAs2025 #AI #MachineLearning #ContinualLearning #LifelongLearning #ResearchConference #CallForPapers #NonStationaryLearning

A year later and our work on Loss of Plasticity is finally published, in Nature no less! The Nature version is totally rewritten and has many new results: nature.com/articles/s41586-0… Congratulations to the authors: @s_dohare @JFernandoHG @LanceLan3 @rahman_parash @rupammahmood

It’s a deep learning problem that was ‘hidden in plain sight:’ A new Nature paper by Amii researchers explores why continual learning models can all of a sudden stop working, and what to do about it: hubs.la/Q02M4h8G0 #AI #ContinualLearning #MachineLearning

I couldn't be prouder of my colleagues at the @UAlberta! The work led by @s_dohare, in collaboration w/ J. F. H.-Garcia, @LanceLan3, @rahman_parash, @rupammahmood, & @RichardSSutton on continual learning and loss of plasticity is now published at @Nature! nature.com/articles/s41586-0…