reinforcement-learning

Further Reading. Thumbail original image used credit: Adobe Stock Image. Graph from: Scheming in the wild: detecting real-world AI scheming incidents with open-source intelligence. Shutdown resistance in reasoning models. https://palisaderesearch.org/blog/shu… Natural emergent misalignment from reward hacking in production RL https://arxiv.org/html/2511.18397v1 Scheming in the wild: detecting rea…

Computer Science > Machine Learning Title:MaxProof: Scaling Mathematical Proof with Generative-Verifier RL and Population-Level Test-Time Scaling View PDF HTML (experimental)Abstract:We present MaxProof, a population-level test-time scaling framework for competition-level mathematical proof in the MiniMax-M3 series. M3 first trains three proof-oriented capabilities -- proof generation, proof veri…

Nature Communications, Published online: 09 June 2026; doi:10.1038/s41467-026-74004-0 Model Predictive Task Sampling (MPTS) enables efficient, risk-aware task selection for meta-RL, domain randomization, and foundation model finetuning by predicting adaptation difficulty without exhaustive evaluation, improving robustness while reducing compute and interaction costs.

Nature Communications, Published online: 08 June 2026; doi:10.1038/s41467-026-72491-9 This work introduces a generalizable control system that enables rapid adaptation across 33 soft robot configurations via reinforcement learning in a shared Koopman embedding space, enabling real-world skills in carpentry and bartending style tasks.

Scientific Reports, Published online: 08 June 2026; doi:10.1038/s41598-026-55166-9 A single reinforcement learning model to unify habit formation and Pavlovian-instrumental interaction

Recap. In Part 1 we landed on the core idea of SDAR ( arXiv:2605.15155 ): keep RL as the backbone, bolt on a privileged teacher for dense token-level guidance, and put a sigmoid gate between them so the student amplifies the teacher's confident advice and softens its noisy rejections. We also said the quiet part out loud - this is not a Bedrock fine-tuning checkbox. This part is the blueprint. Th…

The Core Problem You shipped an AI agent. It works in demos. Then it runs 10,000 times in production, and you realize you have no idea which runs were good. This is the agent evaluation problem, and most teams approach it backwards. They reach for model-as-judge ("ask GPT-4 if the output is good") because it feels natural. But this is like using a microscope when you needed a ruler first. Here's …

How a simple choice shapes exploration, safety, and efficiency The post The Fundamental Choice in Reinforcement Learning: On‑Policy vs. Off‑Policy appeared first on Towards Data Science .

Human-Aligned Decision Transformers for satellite anomaly response operations with inverse simulation verification A Discovery Born from a Late-Night Simulation It was 2:47 AM, and I was staring at a terminal window filled with telemetry data from a simulated satellite constellation. For weeks, I had been experimenting with Decision Transformers—a class of models that frame reinforcement learning…

Single-turn chatbots are evolving into long-running agents that can reason, maintain context, use tools, and run efficiently across many turns to complete...

Hello everyone! It has been a busy week, but I've made some exciting progress on my machine learning journey. Here is what I've been up to: Kaggle Orbit Wars & AWS I completed the baseline implementation for the Kaggle Orbit Wars competition and initially hit a score of around 1030. My score has dipped slightly over the past few days, so I am currently brainstorming ways to improve it. This week …

Pitolisant, a histamine H3 receptor inverse agonist, improved recognition memory, working memory performance, and reinforcement learning in healthy adults. The findings suggest that histamine helps shape how the brain stabilizes new memories, accumulates evidence for decisions, and avoids overreacting to negative outcomes.

All tests run on an 8-year-old MacBook Air. This is Part 3 of my series on training a card game AI with Google Colab. Part 1: Google Colab basics My Old MacBook Air Couldn't Handle It — So I Used Google Colab to Train an AI＃1 hiyoyo hiyoyo hiyoyo Follow May 21 My Old MacBook Air Couldn't Handle It — So I Used Google Colab to Train an AI＃1 # ai # python # googlecolab # rust Comments Add Comment 3 …

Making Dr GRPO go brrr I wrote a fused decode-attention kernel for an RL training loop, got it 2.2× faster than the SDPA path it replaces at the microbenchmark level, dropped it into HuggingFace's generate , and watched the decode step get nearly 3× slower. The kernel was doing exactly what the microbench said it would. The integration broke an auto-compile path that the baseline was quietly bene…

Accurate system identification is essential for modeling and controlling vehicle dynamics. This dissertation explores the application of Parameter Informed Reinforcement Learning (PIRL) as a novel approach to system identification (SYSID). PIRL integrates prior system knowledge, such as physical parameters, into reinforcement learning (RL) frameworks to improve estimation accuracy. The study begi…

When I started building my first harness around a coding agent, I did not picture an onion. I pictured a constraint system. The LLM, on its own, can do almost anything. It can write code, hallucinate APIs, edit the wrong file, run a shell command in a directory it should not be in, decide a test failure is acceptable and move on. The space of things it might do on any given turn is enormous. The …

So during my 4th Year of my college my team had decided to build a Personalized AI Assistant that can understand the user's behavior and give results accordingly. We had implemented reinforcement learning in the backend server, so based on the feedback given by the user it gives the output. During the later stages of the project I glimpsed on something called RAG (Retrieval Augmented Generation).…

About this series. I'm going to take a fresh paper - Self-Distilled Agentic Reinforcement Learning (SDAR, arXiv:2605.15155 ) - and architect it end to end on AWS: the system design, the actual gate code, the evaluation plan, and a brutally honest cost model. What I'm not going to do is wave a benchmark number around. Reproducing a paper like this costs thousands in GPU time, and I'd rather show y…