Create accountLogin

Recent searches

No recent searches

Search options

Only available when logged in.
veganism.social is one of the many independent Mastodon servers you can use to participate in the fediverse.
Veganism Social is a welcoming space on the internet for vegans to connect and engage with the broader decentralized social media community.

Administered by:

Server stats:

214
active users

veganism.social: AboutStatusProfiles directoryPrivacy policy

Mastodon: AboutGet the appKeyboard shortcutsView source codev4.4.0-alpha.4

#reinforcementlearning

2 posts2 participants0 posts today
Public *
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
Antonio

📄 Nuestro último artículo "MELGYM: A dynamic control interface for MELCOR simulations" ha sido publicado en la revista SoftwareX.

🔗 sciencedirect.com/science/arti

Presentamos MELGYM, una interfaz en Python que permite el control interactivo de simulaciones con MELCOR, un código ampliamente utilizado para el análisis de seguridad en instalaciones nucleares como IFMIF-DONES.

#reinforcementlearning#ai#nuclear
Public
N-gated Hacker News

Oh, look! 🎉 Another groundbreaking study in which #academia leans on #buzzwords like "reinforcement learning" to suggest that someday, maybe, #AI will conquer more than just calculus and compiling code. 🤖 It's like a toddler boasting about mastering finger painting and claiming they’ll soon create the next Mona Lisa. 🖼️
arxiv.org/abs/2503.23829 #ReinforcementLearning #GroundbreakingStudy #TechTrends #HackerNews #ngated

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.
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
JesseTong

@lianna Well, most #AIs and #robots in fiction I think their inputs are mostly or fully sensory-based, and they learn in real time through #ReinforcementLearning - esque techniques. AIs like LLMs are frozen in place (they never update and are just replaced over time), and they do not have any meanful interaction to the real world, nor like reflection.

I'd think that robots like #Sophia a few years ago would be more closer to the former than the latter, but #AIBros love conflating the twos.

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
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
Public
Tero Keski-Valkama

How to formulate exploration-exploitation trade-off better than all the hacks on top of Bellman equation?

We can first of all simply estimate the advantage of exploration by Monte-Carlo in a swarm setting: Pitting fully exploitative agents against fully exploitative agents which have the benefit of recent exploration. This can be easily done by lagging policy models.

Of course the advantage of exploration needs to be divided by the cost of exploration, which is linear to the number of agents used in the swarm to explore at a particular state.

Note that the advantage of exploration depends on the state of the agent, so we might want to define an explorative critic to estimate this.

What's beautiful in this formulation is that we can incorporate autoregressive #WorldModels naturally, as the exploitative agents only learn from rewards, but the explorative agents choose their actions in a way which maximizes the improvement of the auto-regressive World Model.

It brings these two concepts together as sides of the same coin.

Exploitation is reward-guided action, exploration is auto-regressive state transition model improvement guided action.

Balancing the two is a swarm dynamic which encourages branching where exploration has an expected value in reward terms. This can be estimated by computing the advantage of exploitative agents utilizing recent exploration versus agents which do not, and returning this advantage to the points of divergence between the two.

#mathematics#ReinforcementLearning#RL
Public
Brandon Rohrer

A fun part of working on a #ReinforcementLearning workbench is that I get to think about how to connect different kinds of agents to different kinds of worlds – representation, interfaces, abstraction.

Something I’m stumbling on is representing models and planners.
Is there such a thing as a planner distinct from a model? Or is planning just something a model does?
In object-oriented programming terms, would a planner be a separate class from a model? Or would it be a method in a model class?

VegansExploreLive feeds
About