Exploring continual learning without replay buffers: Our findings using dynamic

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1s7p0au/tracer_learntodefer_for_llm_classification_with/"> <img alt="TRACER: Learn-to-Defer for LLM

<!-- SC_OFF --><div class="md"><p>The BDH (Dragon Hatchling) paper (arXiv:2509.26507) describes a Hebbian synaptic plasticity mechanism where model weights update during

<!-- SC_OFF --><div class="md"><p>&#x200b;</p> <p>## Abstract</p> <p>Post-training makes language models more decisive without necessarily making them more accurate — and we find

<!-- SC_OFF --><div class="md"><p>Recently, I’ve become fascinated by Continual Learning, especially the idea of AI systems that can continuously adapt and improve from experience

<!-- SC_OFF --><div class="md"><p>I built a small pytorch sampler called <strong>dynabatch</strong> after facing this specific batching issue while fine tuning a NLLB-200 600M

<!-- SC_OFF --><div class="md"><p><strong>TL;DR:</strong> Most RL post-training algorithms optimize the current batch and move on. But after an update, did the new policy actually

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s2e0d5/built_an_opensource_llm_router_for_consumer_gpus/"> <img alt="Built an open-source LLM router for

<!-- SC_OFF --><div class="md"><p>Lately, I was creating content about <strong>Support Vector Machines (SVM)</strong>. The reason it was on my radar is that I saw a recent Kaggle