Russ Salakhutdinov @rsalakhu.bsky.social · 28/04/2025New work on automated prompt engineering for personalized text-to-image generation: PRISM: Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation Paper + Code: kellyyutonghe.github.io/prism/ 100
Russ Salakhutdinov @rsalakhu.bsky.social · 23/04/2025blog.ml.cmu.edu/2025/04/23/c...blog.ml.cmu.eduCarnegie Mellon University at ICLR 2025CMU researchers are presenting 143 papers at the Thirteenth International Conference on Learning Representations (ICLR 2025), held from April 24 - 28 at the Singapore EXPO. Here is a quick overview of... 010
Russ Salakhutdinov @rsalakhu.bsky.social · 09/04/2025blog.ml.cmu.edu/2025/04/09/c...blog.ml.cmu.eduCopilot Arena: A Platform for CodeFigure 1. Copilot Arena is a VSCode extension that collects human preferences of code directly from developers. As model capabilities improve, large language models (LLMs) are increasingly integra... 000
Russ Salakhutdinov @rsalakhu.bsky.social · 05/04/2025www.llama.com Llama4 models are out! Open sourced! Check them out: “Native multimodality, mixture-of-experts models, super long context windows, step changes in performance, and unparalleled efficiency. All in easy-to-deploy sizes custom fit for how you want to use it”llama.comLlamaThe open-source AI models you can fine-tune, distill and deploy anywhere. Choose from our collection of models: Llama 4 Maverick and Llama 4 Scout. 061
Russ Salakhutdinov @rsalakhu.bsky.social · 19/02/2025New work #ICLR2025 on “Dissecting Adversarial Robustness of Multimodal LM Agents” that shows that one can successfully break latest agents that use black-box frontier LLMs, including agents that perform reflection and tree search. Paper + Code + Data: chenwu.io/attack-agent/chenwu.ioDissecting Adversarial Robustness of Multimodal LM AgentsDissecting Adversarial Robustness of Multimodal LM Agents 110
Russ Salakhutdinov @rsalakhu.bsky.social · 19/02/2025Excited to be at the GenAI Summit at UCSD! I'll be sharing our latest work on VisualWebArena, inference-time tree search, and Internet-scale training of LLM Agents. genaisummit2025.ucsd.edugenaisummit2025.ucsd.eduGenAI Summit 2025#GenAIUCSD25 000
Russ Salakhutdinov @rsalakhu.bsky.social · 12/02/20251/4 New Work on InSTA: A pipeline for Internet-scale training of web agents across 150k diverse websites without human annotations. Paper + Code: data-for-agents.github.io Environment: github.com/data-for-age... 120
Russ Salakhutdinov @rsalakhu.bsky.social · 10/02/20251/3 New work on Self-Regulation and Requesting Interventions: Enabling agents with a limited intervention budget to decide when to seek help: Paper: soyeonm.github.io/self_reg/ We develop an offline framework that trains a helper policy to request interventions by combining LLM-based PRMs with RL 140
Russ Salakhutdinov @rsalakhu.bsky.social · 10/01/2025blog.ml.cmu.edu/2025/01/08/o...blog.ml.cmu.eduOptimizing LLM Test-Time Compute Involves Solving a Meta-RL ProblemFigure 1: Training models to optimize test-time compute and learn "how to discover" correct responses, as opposed to the traditional learning paradigm of learning "what answer" to output. The major... 030
Russ Salakhutdinov @rsalakhu.bsky.social · 02/01/2025blog.ml.cmu.edu/2025/01/02/i...blog.ml.cmu.eduInductive biases of neural network modularity in spatial navigationTL;DR: The brain may have evolved a modular architecture for daily tasks, with circuits featuring functionally specialized modules that match the task structure. We hypothesize that this architecture ... 000
Reposted by Russ SalakhutdinovPaul Vicol @paulvicol.bsky.social · 19/12/2024🌲 Ruslan Salakhutdinov (@rsalakhu.bsky.social) from CMU (@scsatcmu.bsky.social) opened the workshop with a talk on Tree Search for Language Model Agents. Timestamp 36:20 in neurips.cc/virtual/2024... 📎 arxiv.org/abs/2407.01476 #NeurIPS2024 #AdaptiveFoundationModels 111
Reposted by Russ SalakhutdinovPaul Vicol @paulvicol.bsky.social · 19/12/2024🎉 Had fun at #NeurIPS2024 Workshop on #AdaptiveFoundationModels! 🚀 Speakers: @rsalakhu.bsky.social @sedielem.bsky.social Kate Saenko, Matthias Bethge / @vishaalurao.bsky.social Minjoon Seo, Bing Liu, Tianqi Chen 🌐Posters: adaptive-foundation-models.org/papers 🎬 neurips.cc/virtual/2024... 🧵Recap! 1102
Russ Salakhutdinov @rsalakhu.bsky.social · 15/12/2024With my amazing students and collaborators at @neuripsconf.bsky.social in Vancouver! 000
Russ Salakhutdinov @rsalakhu.bsky.social · 07/12/2024blog.ml.cmu.edu/2024/12/06/s...blog.ml.cmu.eduScribeAgent: Fine-Tuning Open-Source LLMs for Enhanced Web NavigationTL;DR: LLM web agents are designed to predict a sequence of actions to complete a user-specified task. Most existing agents are built on top of general-purpose, proprietary models like GPT-4 and rely ... 021
Russ Salakhutdinov @rsalakhu.bsky.social · 03/12/2024Carnegie Mellon University at NeurIPS 2024 – Machine Learning Blog | ML@CMU | Carnegie Mellon University Carnegie Mellon University is proud to present 194 papers at the 38th conference on Neural Information Processing Systems (NeurIPS 2024) blog.ml.cmu.edu/2024/12/02/c...blog.ml.cmu.eduCarnegie Mellon University at NeurIPS 2024Carnegie Mellon University is proud to present 194 papers at the 38th conference on Neural Information Processing Systems (NeurIPS 2024), held from December 10-15 at the Vancouver Convention Center. H... 130
Russ Salakhutdinov @rsalakhu.bsky.social · 03/12/20241/2 New work on Evaluating Deep Unlearning in Large Language Models. Paper: arxiv.org/abs/2410.15153 Unlearning specific facts in LLMs is challenging because the facts in LLMs can be deduced from each other. This work proposes a framework for deep unlearning of facts that are interrelated.arxiv.orgEvaluating Deep Unlearning in Large Language ModelsMachine unlearning is a key requirement of many data protection regulations such as GDPR. Prior work on unlearning has mostly considered superficial unlearning tasks where a single or a few related pi... 100