PIRL: From Open-Loop Exploration to Closed-Loop Reinforcement Learning [R]

<!-- 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>Hi,</p> <p>I’ve been doing some work in the continual learning space and wanted to share an open-source framework we put together called

arXiv:2607.03702v1 Announce Type: new Abstract: Large language model (LLM) agents have shown strong decision-making capabilities in long-horizon interactive tasks, yet they still

arXiv:2605.08354v1 Announce Type: new Abstract: Aligning multimodal generative models with human preferences demands reward signals that respect the compositional,

arXiv:2604.05013v1 Announce Type: cross Abstract: Current LLM coding agents are predominantly trained on composite benchmarks (e.g., bug fixing), which often leads to

<!-- SC_OFF --><div class="md"><p>Been heads down building graphify since April 5th. The idea: instead of stuffing your entire codebase into context every session, build a

arXiv:2609.35897v1 Announce Type: new Abstract: The pursuit of recursive self-improvement (RSI) toward general intelligence is divided between macro-level language model scaling

arXiv:2606.31392v1 Announce Type: new Abstract: Tool-augmented vision-language models (VLMs) can solve multimodal, multi-step tasks by calling external tools, yet they remain