A debugger for RL reward functions that detects reward hacking during training [

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s9ciae/reward_hacking_when_reason_tuning/"> <img alt="Reward hacking when reason tuning

<!-- SC_OFF --><div class="md"><p>I've been working on MDA (Modular Dynamic Architecture), an online associative memory system for LLMs. Here's what I learned building it.</p>

<!-- SC_OFF --><div class="md"><p>Hi, <a href="/r/MachineLearning">r/MachineLearning</a>: has much research been done in large-scale training scenarios where undesirable data has

<!-- SC_OFF --><div class="md"><p>Disclosure: I built LabelSets (labelsets.ai). Sharing the technical approach behind how we score dataset quality.</p> <p>THE PROBLEM</p> <p>Most

<!-- SC_OFF --><div class="md"><p>We just did a big revamp of <strong>WeightsLab</strong> and wanted to share it here.<br /> If you’ve ever spent hours debugging a training run

<!-- SC_OFF --><div class="md"><p>I’ve been working on a reinforcement-learning project called <strong>PolyBot</strong>, where I train an AI to drive the racing game PolyTrack.

<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

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