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#reinforcementlearning

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Victoria Stuart 🇨🇦 🏳️‍⚧️

[AGI discussion, DeepMind] Welcome to the Era of Experience
storage.googleapis.com/deepmin
old.reddit.com/r/MachineLearni

* threshold of new era in AI that promises unprecedented level of ability
* new generation of agents will acquire superhuman capabilities, learning predominantly f. experience
* paradigm shift, accompanied by algorithmic advancements in RL, will unlock new supra-human capabilities

[AGI discussion, DeepMind] Welcome to the Era of Experience https://storage.googleapis.com/deepmind-media/Era-of-Experience%20/The%20Era%20of%20Experience%20Paper.pdf https://old.reddit.com/r/MachineLearning/comments/1k4zr1i/r_deepmind_welcome_to_the_era_of_experience We stand on the threshold of a new era in artificial intelligence that promises to achieve an unprecedented level of ability. A new generation of agents will acquire superhuman capabilities by learning predominantly from experience. ... #Google #DeepMind #AI #AGI #RL #ReinforcementLearning #ML #LLM #AIdiscussion
#Google#DeepMind#AI
Public
Hacker News

Can reinforcement learning for LLMs scale beyond math and coding tasks? Probably

arxiv.org/abs/2503.23829

arXiv.orgCrossing the Reward Bridge: Expanding RL with Verifiable Rewards Across Diverse DomainsReinforcement learning with verifiable rewards (RLVR) has demonstrated significant success in enhancing mathematical reasoning and coding performance of large language models (LLMs), especially when structured reference answers are accessible for verification. However, its extension to broader, less structured domains remains unexplored. In this work, we investigate the effectiveness and scalability of RLVR across diverse real-world domains including medicine, chemistry, psychology, economics, and education, where structured reference answers are typically unavailable. We reveal that binary verification judgments on broad-domain tasks exhibit high consistency across various LLMs provided expert-written reference answers exist. Motivated by this finding, we utilize a generative scoring technique that yields soft, model-based reward signals to overcome limitations posed by binary verifications, especially in free-form, unstructured answer scenarios. We further demonstrate the feasibility of training cross-domain generative reward models using relatively small (7B) LLMs without the need for extensive domain-specific annotation. Through comprehensive experiments, our RLVR framework establishes clear performance gains, significantly outperforming state-of-the-art open-source aligned models such as Qwen2.5-72B and DeepSeek-R1-Distill-Qwen-32B across domains in free-form settings. Our approach notably enhances the robustness, flexibility, and scalability of RLVR, representing a substantial step towards practical reinforcement learning applications in complex, noisy-label scenarios.
#HackerNews#reinforcementlearning#LLMs
Public
Antonio Lieto

Happy birthday to Cognitive Design for Artificial Minds (lnkd.in/gZtzwDn3) that was released 4 years ago!

Since then its ideas have been presented and discussed widely in the research fields of AI/Cognitive Science/Robotics and - nowadays - both the possibilities and the limitations of: #LLMs, #GenerativeAI and #ReinforcementLearning (already envisioned and discussed in the book) have become a common topic of research interests in the AI community and beyond.
Similarly also the topic concerning the evaluation - in human-like and human-level terms - of the current AI systems has become a critical theme related to the problem Anthropomorphic interpretation of AI output (see e.g. lnkd.in/dVi9Qf_k ).
Book reviews have been published on ACM Computing Reviews (2021) lnkd.in/dWQpJdkV and on Argumenta (2023): lnkd.in/derH3VKN

I have been invited to present the content of the book in over 20 official scientific events in international conferences, Ph.D Schools in US, China, Japan, Finland, Germany, Sweden, France, Brazil, Poland, Austria and, of course, Italy.

A news I am happy to share is that Routledge/Taylor & Francis contacted me few weeks ago for a second edition! Stay tuned!

The #book is available in many webstores:
- Routledge: lnkd.in/dPrC26p
- Taylor & Francis: lnkd.in/dprVF2w
- Amazon: lnkd.in/dC8rEzPi

@academicchatter @cognition
#AI #minimalcognitivegrid #CognitiveAI #cognitivescience #cognitivesystems

Public
Teixi

#ACMPrize
#2024ACMPrize
#ACMTuringAward

#AndrewBarto
#RichardSutton

» #ReinforcementLearning
An Introduction
1998
standard reference...cited over 75,000
...
prominent example of #RL
#AlphaGo victory
over best human #Go players
2016 2017
....
recently has been the development of the chatbot #ChatGPT
...
large language model #LLM trained in two phases ...employs a technique called
reinforcement learning from human feedback #RLHF «

aka cheap labor unnamed in papers

awards.acm.org/about/2024-turi

2/2

awards.acm.orgAndrew Barto and Richard Sutton are the recipients of the 2024 ACM A.M. Turing Award for developing the conceptual and algorithmic foundations of reinforcement learning.Andrew Barto and Richard Sutton as the recipients of the 2024 ACM A.M. Turing Award for developing the conceptual and algorithmic foundations of reinforcement learning. In a series of papers beginning in the 1980s, Barto and Sutton introduced the main ideas, constructed the mathematical foundations, and developed important algorithms for reinforcement learning—one of the most important approaches for creating intelligent systems.
Public
nemo™ 🇺🇦

Check out QwQ-32B! 🤖 A 32B parameter model that rivals DeepSeek-R1 in performance, thanks to Reinforcement Learning. It excels in math, coding & general problem-solving. Open-weight on Hugging Face & ModelScope! Explore the future of AGI! ✨
👉 qwenlm.github.io/blog/qwq-32b/ #AI #ReinforcementLearning #MachineLearning #Qwen #OpenSource

Qwen · QwQ-32B: Embracing the Power of Reinforcement LearningQWEN CHAT Hugging Face ModelScope DEMO DISCORD Scaling Reinforcement Learning (RL) has the potential to enhance model performance beyond conventional pretraining and post-training methods. Recent studies have demonstrated that RL can significantly improve the reasoning capabilities of models. For instance, DeepSeek R1 has achieved state-of-the-art performance by integrating cold-start data and multi-stage training, enabling deep thinking and complex reasoning. Our research explores the scalability of Reinforcement Learning (RL) and its impact on enhancing the intelligence of large language models.
Public
Dr. Anna Latour

My colleagues at TU Delft are seeking to hire a postdoc to work on Applied Planning and Scheduling under Uncertainty, with applications in modelling supply chain scenarios for offshore wind farm installation: careers.tudelft.nl/job/Delft-P

careers.tudelft.nlPostdoc in Applied Planning and Scheduling under UncertaintyPostdoc in Applied Planning and Scheduling under Uncertainty
#AcademicMastodon#PostdocLife#Hiring
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