#adaptivedynamicprogramming نتائج البحث
Our latest post explores on-policy distillation, a training approach that unites the error-correcting relevance of RL with the reward density of SFT. When training it for math reasoning and as an internal chat assistant, we find that on-policy distillation can outperform other…
New Stanford + SambaNova + UC Berkeley paper proposes quite a revolutionary idea. 🤯 Proves LLMs can be improved by purely changing the input context, instead of changing weights. Introduces a new method called Agentic Context Engineering (ACE). It helps language models…
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Stanford just pulled off something wild 🤯 They made models smarter without touching a single weight. The paper’s called Agentic Context Engineering (ACE), and it flips the whole fine-tuning playbook. Instead of retraining, the model rewrites itself. It runs a feedback loop…
Most AIs predict words @SentientAGI is teaching them to predict people In MindGames, agents don’t just react; they strategize, bluff, negotiate, and adapt under pressure. It’s not artificial intelligence anymore. It’s adaptive intelligence; where reasoning meets reality.
Model-Free Reinforcement Learning (MFRL) has been alluring, especially with supercharged compute with physics on GPU. However, the methods use 0-th order gradients, and are often not the best optimizers. Can we do better than PPO in continuous control for robotics? Turns out…
🔥 Read our Paper 📚 Inducing Optimality in Prescribed Performance Control for Uncertain Euler–Lagrange Systems 🔗 mdpi.com/2076-3417/13/2… 👨🔬 by Christos Vlachos et al. #adaptivedynamicprogramming #optimalcontrol
#Adaptability is a meta-skill — a force multiplier. According to McKinsey, adaptive traits enhance #performance, boost #creativity, and future-proof your edge. Increase your adaptability by: 1. Switching up a routine. 2. Changing your environment. 3. Disrupting autopilot.
We explore a new dimension in scaling reasoning models in Adaptive Parallel Reasoning APR lets LMs learn to orchestrate both serial & parallel compute E2E via supervised training + RL — w/ better efficiency and scalability than long CoT on Countdown 🧵 arxiv.org/abs/2504.15466
Combining the benefits of RL and SFT with on-policy distillation, a promising approach for training small models for domain performance and continual learning.
Our latest post explores on-policy distillation, a training approach that unites the error-correcting relevance of RL with the reward density of SFT. When training it for math reasoning and as an internal chat assistant, we find that on-policy distillation can outperform other…
AGII develops predictive optimization frameworks to enhance smart contract performance and blockchain execution. Read more: apnews.com/press-release/…
Ren, Xu, Deng: Accelerated Distance-adaptive Methods for H\"{o}lder Smooth an... arxiv.org/abs/2510.22135 Ni, Qiu, Xiao: A projection-free dynamics for nonsmooth composite optimization arxiv.org/abs/2510.22173 en.wikipedia.org/wiki/Mathemati…
Dynamic programming Goldmine ❤️ Dynamic Programming is one of the most important topic of any tech interview process. Found this really amazing blog on LeetCode covering important topics. A Thread 🧵
𝗠𝗼𝗻𝗼𝗹𝗶𝘁𝗵 𝗗𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 We have three types of monoliths. In 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 ones, we have everything bundled together in a layered form. We have also 𝗺𝗼𝗱𝘂𝗹𝗮𝗿 𝗺𝗼𝗻𝗼𝗹𝗶𝘁𝗵𝘀, where we have defined functional boundaries…
If you’ve felt the ground shifting under “prompt engineering,” you’re not imagining it. Two powerful ideas now define how to get real business value from LLMs: Agentic Context Engineering (ACE) make models smarter without retraining by evolving the context itself. K.E.R.N.E.L.…
Atua AI introduces adaptive workflow engines to accelerate Web3 developer productivity and automation. Read more: issuewire.com/atua-ai-introd…
𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 Goldmine ❤️ Dynamic Programming is one of the most important topic of any tech interview process. Found this really amazing blog on Codeforces covering all important topics. A Thread 🧵
Anyone working on adaptive optimization methods and replacements for Adam should check this paper.
🔥 Read our Paper 📚 Inducing Optimality in Prescribed Performance Control for Uncertain Euler–Lagrange Systems 🔗 mdpi.com/2076-3417/13/2… 👨🔬 by Christos Vlachos et al. #adaptivedynamicprogramming #optimalcontrol
🔥 Read our Highly Cited Paper 📚 Adaptive Dynamic Programming-Based Cross-Scale Control of a Hydraulic-Driven Flexible Robotic Manipulator 🔗 mdpi.com/2076-3417/13/5… 👨🔬 by Xiaohua Wei et al. #adaptivedynamicprogramming #rigidflexiblemanipulator
Scientists develop an #AdaptiveDynamicProgramming method based on #InternalModelPrinciple to achieve #adaptive and #optimal #DiscreteTime #OutputFeedback. Find more details in #IEEECAA #JournalofAutomaticaSinica: ow.ly/9yir50QvKfW
🔥 Read our Paper 📚 Inducing Optimality in Prescribed Performance Control for Uncertain Euler–Lagrange Systems 🔗 mdpi.com/2076-3417/13/2… 👨🔬 by Christos Vlachos et al. #adaptivedynamicprogramming #optimalcontrol
Scientists develop an #AdaptiveDynamicProgramming method based on #InternalModelPrinciple to achieve #adaptive and #optimal #DiscreteTime #OutputFeedback. Find more details in #IEEECAA #JournalofAutomaticaSinica: ow.ly/9yir50QvKfW
🔥 Read our Highly Cited Paper 📚 Adaptive Dynamic Programming-Based Cross-Scale Control of a Hydraulic-Driven Flexible Robotic Manipulator 🔗 mdpi.com/2076-3417/13/5… 👨🔬 by Xiaohua Wei et al. #adaptivedynamicprogramming #rigidflexiblemanipulator
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