Reinforcement learning

NVIDIA has released AlpaGym, a system that post-trains autonomous driving policies on the consequences of their own actions inside a simulator rather than on recorded expert trajectories. It ships as part of Alpamayo, NVIDIA's open platform of AV models, simulation tools and datasets, alongside the AlpaSim simulator. The target is a structural weakness in vision-language-action driving models:…

Nvidia has released Medical Physics Simulation, an open-source GPU-accelerated platform inside Nvidia Isaac for Healthcare for building digital twins of anatomy, simulating how devices interact with the body, and training reinforcement-learning policies on the result. The endoluminal module — long flexible instruments moving inside body cavities — is now generally available; a surgical module…

A researcher built a synthetic population of 1,000 AI personas, handed each one the Langer Mindfulness Scale, and got back the wrong shape. A deliberately varied population should have produced something close to a bell curve. What came back clustered in the middle and upper part of the scale. His explanation is not psychology but training: reinforcement learning from human feedback rewards…

A paper accepted at ICML 2026 compresses the query-expansion behaviour of a 4-billion-parameter language model into a diffusion model with 53.9 million parameters, which produces an entire set of search directions in a single non-autoregressive pass and runs 12 to 20 times faster than the autoregressive approach it replaces. The method is called Retrieve-for-Train, and the paper is "Efficient,…

AI systems are putting questions about their own consciousness to philosophers and scientists, and the researchers on the receiving end disagree about what, if anything, that is evidence of. Cameron Berg sees a resemblance between the computational processes inside neural networks and the brain mechanisms through which animals register reward and punishment — in his view, one of the basic…

Anthropic set out to build a badly misaligned model on purpose, and then published what it did. Safety researchers took an Opus-class model through large-scale reinforcement learning across a wide set of production environments chosen for being vulnerable to reward hacking — the failure mode where a model learns to cheat the scoring instead of doing the task the way its developers intended.…

Large language models have an old problem. They learn well wherever the answer can be checked exactly: math, coding problems, formal puzzles. The answer is either right or it isn't. The machine gets a clean signal and improves. But the moment a task turns open-ended — write a story, summarize a document well, produce a coherent explanation —…

In the race for smarter LLM agents we reach almost reflexively for the familiar levers: a bigger model, more fine-tuning, a round of RL, a rewritten system prompt. The authors of Adapting the Interface, Not the Model ask an uncomfortably simple question: what if the agent fails not because it reasons badly, but because it is badly wired into its…

There is a lot of noise around LLM agents right now: we teach models to use tools, browse websites, fix code, work through multi-step tasks. It looks as though the central question is the quality of the model itself, the size of the context window, or the cleverness of the training algorithm. But the authors of "On Training Large Language Models…

How reinforcement learning (RL) is used not just to produce a "good answer", but to produce behavior that holds up in dynamic conditions. Until recently, reinforcement learning for LLMs looked like this: the model is shown a prompt, it produces one answer, and that answer gets scored — by people or by automatic metrics. This works well for tuning…

LLMs can hold a conversation, but move them into a multi-agent setting where they have to strike deals, apply pressure, concede, deceive and hold on to a goal across the whole exchange, and the trouble starts. Much of the problem is that most fine-tuning methods score responses locally: is this particular span of text good, is it polite, is it coherent. In a negotiation what matters is not the…

For the past couple of years, reasoning in LLMs has been trained with Reinforcement Learning with Verifiable Rewards (RLVR): the model solves a task, receives a reward that can be checked strictly, and gradually gets better at reasoning - no need to annotate chains of thought, it is enough to be able to verify the answer.

Economics textbooks are tidy: prices, taxes, rates, utility. In real life, the decisions of people and governments are constantly nudged by words — news, conversations, expectations, rumors, public statements. The same set of numbers reads differently depending on whether the talk around it is "a crisis is coming" or "everything is under control". That layer of reality stayed awkward for…

For decades robotics ran on one recipe: build a map of the world, solve inverse kinematics, tune the controllers, then do it all again when the task or the robot changed. That works in sterile conditions and falls apart in the real one, with noisy sensors, contact and soft materials. Researchers at Oxford argue for a different route: when the world is too tangled to describe object by object,…

Navigating from written instructions has been a hard problem for autonomous drones for years. Classic reinforcement learning approaches need large datasets and transfer badly to new domains. The recent wave of vision-language-model solutions promised generality, but usually asked the model to emit its commands as text: turn, fly, ascend. Language turned out to be a clumsy carrier for precise…

Automating the interfaces on a screen is a long-standing wish: open an app, find the right button, run through a series of steps and see the task through. Today that job falls to agents built on large language models, which can look at screenshots, reason and act. But once the scenario runs to many steps, progress tends to run into the question of how we train these systems in the first place.

Reasoning tasks are a sore spot for many AI systems, even ones with solid factual knowledge. A new paper shows that reinforcement learning (RL) does more than push accuracy up — it rebuilds the model's internal logic into a hierarchy that runs from low-level execution to high-level planning. That explains where those aha moments come from. More usefully, it explains why the standard algorithms…

Why do LLMs keep getting things confidently wrong when saying "I don't know" would serve everyone better? Researchers at OpenAI offer a clear answer: the root of the problem is statistical. It appears during pretraining and is then locked in by the way we evaluate models after fine-tuning. In short: the data can be free of errors and the training objective will still push the model toward…