On Building Agents From First Principles (via @athleticKoder)

kept by eddie may 21

A step-by-step methodology for building AI agents by defining environments, generating teacher trajectories, fine-tuning student models, and iteratively improving them with reinforcement learning.

[2604.27505] Leveraging Verifier-Based Reinforcement Learning in Image Editing
Edit-R1 uses a chain-of-thought reasoning reward model, trained with Group Contrastive Preference Optimization, to provide fine-grained, interpretable rewards that improve image-editing model performance.
also on #reinforcement-learning, #machine-learning, #ai-research
joke-generator
Training a joke generator on Kimi K2 using rubric-based RL that decomposes humor into verifiable properties like specificity and commitment.
also on #reinforcement-learning, #machine-learning, #ai-research