Asymmetry of verification and verifier’s rule — Jason Wei

Training AI Agents with RL | Unsloth Documentation

Learn how to train AI agents for real-world tasks using Reinforcement Learning (RL).

Prediction: AI will make formal verification go mainstream — Martin Kleppmann’s blog

Much has been said about the effects that AI will have on software development, but there is an angle I haven’t seen talked about: I believe that AI will bring formal verification, which for decades has been a bit of a fringe pursuit, into the software engineering mainstream.

The argument against AI agents and unnecessary automation

Opinion: OpenAI's Operator a solution in search of a problem

Look-ahead Reasoning with a Learned Model in Imperfect Information Games

Test-time reasoning significantly enhances pre-trained AI agents' performance. However, it requires an explicit environment model, often unavailable or overly complex in real-world scenarios....

Reward is not the optimization target — LessWrong

TurnTrout discusses a common misconception in reinforcement learning: that reward is the optimization target of trained agents. He argues reward is b…

Why Tool AIs Want to Be Agent AIs

AIs limited to pure computation (Tool AIs) supporting humans, will be less intelligent, efficient, and economically valuable than more autonomous reinforcement-learning AIs (Agent AIs) who act on their own and meta-learn, because all problems are reinforcement-learning problems.

Reinforcement Learning from Human Feedback

The authoritative guide for Reinforcement learning from human feedback, alignment, and post-training LLMs. Aligning AI models to human preferences helps them become safer, smarter, easier to use, and tuned to the exact style the creator desires. Reinforcement Learning From Human Feedback (RHLF) is the process for using human responses to a model’s output to shape its alignment, and therefore its behavior. In Reinforcement Learning from Human Feedback, author Nathan Lambert blends diverse perspectives from fields like philosophy and economics with the core mathematics and computer science of RLHF to provide a practical guide you can use to apply RLHF to your models. In Reinforcement Learning from Human Feedback you’ll discover: How today’s most advanced AI models are taught from human feedback How large-scale preference data is collected and how to improve your data pipelines A comprehensive overview with derivations and implementations for the core policy-gradient methods used to train AI models with reinforcement learning (RL) Direct Preference Optimization (DPO), direct alignment algorithms, and simpler methods for preference finetuning How RLHF methods led to the current reinforcement learning from verifiable rewards (RLVR) renaissance Tricks used in industry to round out models, from product, character or personality training, AI feedback, and more How to approach evaluation and how evaluation has changed over the years Standard recipes for post-training combining more methods like instruction tuning with RLHF Behind-the-scenes stories from building open models like Llama-Instruct, Zephyr, Olmo, and Tülu After ChatGPT used RLHF to become production-ready, this foundational technique exploded in popularity. In Reinforcement Learning from Human Feedback, AI expert Nathan Lambert gives a true industry insider's perspective on modern RLHF training pipelines, and their trade-offs. Using hands-on experiments and mini-implementations, Nathan clearly and concisely introduces the alignment techniques that can transform a generic base model into a human-friendly tool.

Refine — AI Verification Trusted by World-Class Experts

Good decisions require verified quality. Refine devotes hours of frontier compute to protect your work and reputation from fixable mistakes.

Agent Skills

AI coding agents take the shortest path to done, which usually means skipping the specs, tests, and reviews that make software reliable at scale. Agent Skill...

Position: Don't Just "Fix it in Post": A Science of AI Must Study Training Dynamics

What would it mean to have a scientific understanding of AI? Models are not static objects: they are snapshots of time-evolving processes shaped by data, objectives, architectures, and optimization dynamics. Yet much of AI research treats models as fixed artifacts, analyzing behaviors after training rather than asking why they emerge. This position paper argues that a science of AI must move beyond post-hoc fixes and study the training dynamics that produce model behavior. Such a science should support progressively stronger forms of understanding: predicting outcomes from early training signals, intervening when trajectories go wrong, and ultimately designing training procedures that more reliably produce desired properties. Scaling laws have made prediction routine for loss; the challenge is extending this success to capabilities, biases, robustness, and safety-relevant behaviors. We articulate requirements for such theories grounded in the history and philosophy of science, examine progress in mechanistic interpretability, fairness, memorization, and simplicity bias, and identify concrete open problems.

Is AI Reasoning Right for the Wrong Reasons? | Quanta Magazine

The idea that artificial intelligence can “reason” is more intuitive than ever. But intuitions can be wrong, and the science is far from settled.

Vehicle: Bridging the Embedding Gap in the Verification of...

Neuro-symbolic programs, i.e. programs containing both machine learning components and traditional symbolic code, are becoming increasingly widespread. Finding a general methodology for verifying...

How does AI impact skill formation?

Two days ago, the Anthropic Fellows program released a paper called How AI Impacts Skill Formation. Like other papers on AI before it, this one is being treated as proof that AI makes you slower and dumber. Does it prove that?

Understanding Confidence Threshold in AI Systems

Learn how confidence threshold works as a decision boundary in AI systems. Discover the key mechanics that determine automated processing versus human review.