DRL is a paradigm in which models learn representations that can identify and disentangle the underlying factors hidden in observed data. This enhances key aspects of machine learning tasks, making them more explainable, generalizable, and controllable.

The authors of this paper present a comprehensive review of DRL, examining its motivations, definitions, methodologies, evaluation practices, applications, and model design considerations.

#Interesting #researchpaper Disentangled Representation Learning https://t.co/kF4uunlx5C #datascience #AI #ML #GenAI #DRL

While there are still important limitations to address, this represents a significant step forward. As publicly available training data becomes increasingly scarce, enabling models to generate their own high-quality data could be a key direction for the future.

Their experiments show an intriguing trend — the self-adapting approach continues to improve performance, even approaching the upper bound achieved by supervised methods.

The authors of this paper present a novel framework that enables #LLMs to self-adapt by generating their own fine-tuning data and update directives.

#Interesting #researchpaper Self-Adapting Language Models https://t.co/BEQLkhlyZS #datascience #AI #GenAI #LLMs

A key finding is that LRMs fail to develop generalizable reasoning capabilities beyond certain complexity thresholds, and standard LLMs outperform LRMs at low complexity.

This work moves beyond math and coding benchmarks by evaluating Large Reasoning Models in controllable puzzle environments, allowing precise complexity control and analysis of both answers and reasoning to reveal how LRMs think.

#Interesting #researchpaper The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity https://t.co/tbxT4WpdLI #datascience #AI #LLMs #LRMs #GenAI #reasoning

A key advantage of this approach is its fully unsupervised nature, allowing it to generalize without relying on human labels, reward signals, or other model supervision. Also, EBTs inherently express uncertainty — an essential capability for making cautious and robust decisions.

Energy-Based Transformers (EBTs) are trained to assign an energy (unnormalized probability) to each input and candidate prediction pair, enabling inference-time predictions through iterative energy minimization —  a process that closely resembles thinking.

As interest grows in developing foundational models capable of reasoning — particularly by scaling inference compute — it becomes increasingly clear that reinforcement learning based training is most effective in domains where rule-based rewards can reliably validate outputs.

#Interesting #researchpaper Energy-Based Transformers (EBTs) are Scalable Learners and Thinkers https://t.co/vk92XfJvDZ #datascience #AI #LLMs #GenAI #reasoning

While it currently has limitations with transformer architectures, I highly recommend this talk to anyone exploring large model deployment on edge.

Insightful webinar on real-time compression and decompression of deep learning models based on resource availability. This framework, though not yet published, offers a promising solution to the fixed memory constraints faced when deploying large models on edge devices.

#Interesting #ACMTechTalk Dynamic Neural Network Compression for Scalable AI Deployment https://t.co/Z0b2P0QYMo #AI #datascience #machinelearning #deeplearning

By comparing various optimization methods for a #BMW assembly line scheduling problem, the authors found that combining quantum-inspired model-based optimization with traditional black-box techniques can yield lower-cost solutions in some cases.

#Interesting #researchpaper Quantum-Inspired Optimization for Industrial Scale Problems https://t.co/iKTiaGZke1 #AI #datascience #optimization

Even though this approach still has many limitations, it is a very important step towards understanding the black box.