7 ChatGPT Tricks to Automate Your Data TasksKDnuggets This article explores how to transform ChatGPT from a chatbot into a powerful data assistant that streamlines the repetitive, the tedious, and the complex.
This article explores how to transform ChatGPT from a chatbot into a powerful data assistant that streamlines the repetitive, the tedious, and the complex. Read More
Maximizing the efficiency of human feedback in AI alignment: a comparative analysiscs.AI updates on arXiv.org arXiv:2511.12796v2 Announce Type: replace-cross
Abstract: Reinforcement Learning from Human Feedback (RLHF) relies on preference modeling to align machine learning systems with human values, yet the popular approach of random pair sampling with Bradley-Terry modeling is statistically limited and inefficient under constrained annotation budgets. In this work, we explore alternative sampling and evaluation strategies for preference inference in RLHF, drawing inspiration from areas such as game theory, statistics, and social choice theory. Our best-performing method, Swiss InfoGain, employs a Swiss tournament system with a proxy mutual-information-gain pairing rule, which significantly outperforms all other methods in constrained annotation budgets while also being more sample-efficient. Even in high-resource settings, we can identify superior alternatives to the Bradley-Terry baseline. Our experiments demonstrate that adaptive, resource-aware strategies reduce redundancy, enhance robustness, and yield statistically significant improvements in preference learning, highlighting the importance of balancing alignment quality with human workload in RLHF pipelines.
arXiv:2511.12796v2 Announce Type: replace-cross
Abstract: Reinforcement Learning from Human Feedback (RLHF) relies on preference modeling to align machine learning systems with human values, yet the popular approach of random pair sampling with Bradley-Terry modeling is statistically limited and inefficient under constrained annotation budgets. In this work, we explore alternative sampling and evaluation strategies for preference inference in RLHF, drawing inspiration from areas such as game theory, statistics, and social choice theory. Our best-performing method, Swiss InfoGain, employs a Swiss tournament system with a proxy mutual-information-gain pairing rule, which significantly outperforms all other methods in constrained annotation budgets while also being more sample-efficient. Even in high-resource settings, we can identify superior alternatives to the Bradley-Terry baseline. Our experiments demonstrate that adaptive, resource-aware strategies reduce redundancy, enhance robustness, and yield statistically significant improvements in preference learning, highlighting the importance of balancing alignment quality with human workload in RLHF pipelines. Read More
How We Learn Step-Level Rewards from Preferences to Solve Sparse-Reward Environments Using Online Process Reward LearningMarkTechPost In this tutorial, we explore Online Process Reward Learning (OPRL) and demonstrate how we can learn dense, step-level reward signals from trajectory preferences to solve sparse-reward reinforcement learning tasks. We walk through each component, from the maze environment and reward-model network to preference generation, training loops, and evaluation, while observing how the agent gradually improves
The post How We Learn Step-Level Rewards from Preferences to Solve Sparse-Reward Environments Using Online Process Reward Learning appeared first on MarkTechPost.
In this tutorial, we explore Online Process Reward Learning (OPRL) and demonstrate how we can learn dense, step-level reward signals from trajectory preferences to solve sparse-reward reinforcement learning tasks. We walk through each component, from the maze environment and reward-model network to preference generation, training loops, and evaluation, while observing how the agent gradually improves
The post How We Learn Step-Level Rewards from Preferences to Solve Sparse-Reward Environments Using Online Process Reward Learning appeared first on MarkTechPost. Read More
Google DeepMind Researchers Introduce Evo-Memory Benchmark and ReMem Framework for Experience Reuse in LLM AgentsMarkTechPost Large language model agents are starting to store everything they see, but can they actually improve their policies at test time from those experiences rather than just replaying context windows? Researchers from University of Illinois Urbana Champaign and Google DeepMind propose Evo-Memory, a streaming benchmark and agent framework that targets this exact gap. Evo-Memory evaluates
The post Google DeepMind Researchers Introduce Evo-Memory Benchmark and ReMem Framework for Experience Reuse in LLM Agents appeared first on MarkTechPost.
Large language model agents are starting to store everything they see, but can they actually improve their policies at test time from those experiences rather than just replaying context windows? Researchers from University of Illinois Urbana Champaign and Google DeepMind propose Evo-Memory, a streaming benchmark and agent framework that targets this exact gap. Evo-Memory evaluates
The post Google DeepMind Researchers Introduce Evo-Memory Benchmark and ReMem Framework for Experience Reuse in LLM Agents appeared first on MarkTechPost. Read More
New control system teaches soft robots the art of staying safeMIT News – Machine learning MIT CSAIL and LIDS researchers developed a mathematically grounded system that lets soft robots deform, adapt, and interact with people and objects, without violating safety limits.
MIT CSAIL and LIDS researchers developed a mathematically grounded system that lets soft robots deform, adapt, and interact with people and objects, without violating safety limits. Read More
The Machine Learning “Advent Calendar” Day 2: k-NN Classifier in ExcelTowards Data Science Exploring the k-NN classifier with its variants and improvements
The post The Machine Learning “Advent Calendar” Day 2: k-NN Classifier in Excel appeared first on Towards Data Science.
Exploring the k-NN classifier with its variants and improvements
The post The Machine Learning “Advent Calendar” Day 2: k-NN Classifier in Excel appeared first on Towards Data Science. Read More
JSON Parsing for Large Payloads: Balancing Speed, Memory, and ScalabilityTowards Data Science Benchmarking JSON libraries for large payloads
The post JSON Parsing for Large Payloads: Balancing Speed, Memory, and Scalability appeared first on Towards Data Science.
Benchmarking JSON libraries for large payloads
The post JSON Parsing for Large Payloads: Balancing Speed, Memory, and Scalability appeared first on Towards Data Science. Read More
How to Vibe Code on a BudgetKDnuggets What if I told you that a powerful vibe coding workflow on par with Claude Code can cost you less than $10? Let me prove it.
What if I told you that a powerful vibe coding workflow on par with Claude Code can cost you less than $10? Let me prove it. Read More
How to Generate QR Codes in PythonTowards Data Science A beginner-friendly tutorial exploring the Python “qrcode” Package
The post How to Generate QR Codes in Python appeared first on Towards Data Science.
A beginner-friendly tutorial exploring the Python “qrcode” Package
The post How to Generate QR Codes in Python appeared first on Towards Data Science. Read More
China’s DeepSeek V3.2 AI model achieves frontier performance on a fraction of the computing budgetAI News While tech giants pour billions into computational power to train frontier AI models, China’s DeepSeek has achieved comparable results by working smarter, not harder. The DeepSeek V3.2 AI model matches OpenAI’s GPT-5 in reasoning benchmarks despite using ‘fewer total training FLOPs’ – a breakthrough that could reshape how the industry thinks about building advanced artificial
The post China’s DeepSeek V3.2 AI model achieves frontier performance on a fraction of the computing budget appeared first on AI News.
While tech giants pour billions into computational power to train frontier AI models, China’s DeepSeek has achieved comparable results by working smarter, not harder. The DeepSeek V3.2 AI model matches OpenAI’s GPT-5 in reasoning benchmarks despite using ‘fewer total training FLOPs’ – a breakthrough that could reshape how the industry thinks about building advanced artificial
The post China’s DeepSeek V3.2 AI model achieves frontier performance on a fraction of the computing budget appeared first on AI News. Read More