Perplexity’s frontier agent products rest on a foundation of know-how and domain expertise packaged in modular Agent Skills. We maintain a carefully curated library of Skills across our technical environments. These Skills include many of the general-purpose utilities powering Perplexity Computer; vertical-specific capabilities in areas such as finance, law, and health; and a very long tail of modules for addressing user needs. Some Skills are infrequently invoked but critical when invoked. To ensure a consistently excellent user experience, Perplexity’s Agents team prioritizes Skill quality just as much as code quality.
The intuitions and best practices required to develop a high-quality Skill differ significantly from those required to build traditional software. The Agents team reviews many pull requests from excellent engineers who develop Skills in the course of their work. The result is almost always numerous comments and suggestions for revision. This is because many useful patterns for writing code become antipatterns in Skill creation. — Read More
Daily Archives: May 5, 2026
Leveraging Verifier-Based Reinforcement Learning in Image Editing
While Reinforcement Learning from Human Feedback (RLHF) has become a pivotal paradigm for text-to-image generation, its application to image editing remains largely unexplored. A key bottleneck is the lack of a robust general reward model for all editing tasks. Existing edit reward models usually give overall scores without detailed checks, ignoring different instruction requirements and causing biased rewards. To address this, we argue that the key is to move from a simple scorer to a reasoning verifier. We introduce Edit-R1, a framework that builds a chain-of-thought (CoT) verifier-based reasoning reward model (RRM) and then leverages it for downstream image editing. The Edit-RRM breaks instructions into distinct principles, evaluates the edited image against each principle, and aggregates these checks into an interpretable, fine-grained reward. To build such an RRM, we first apply supervised fine-tuning (SFT) as a “cold-start” to generate CoT reward trajectories. Then, we introduce Group Contrastive Preference Optimization (GCPO), a reinforcement learning algorithm that leverages human pairwise preference data to reinforce our pointwise RRM. After building the RRM, we use GRPO to train editing models with this non-differentiable yet powerful reward model. Extensive experiments demonstrate that our Edit-RRM surpasses powerful VLMs such as Seed-1.5-VL and Seed-1.6-VL as an editing-specific reward model, and we observe a clear scaling trend, with performance consistently improving from 3B to 7B parameters. Moreover, Edit-R1 delivers gains to editing models like FLUX.1-kontext, highlighting its effectiveness in enhancing image editing. — Read More
Rick's Cafe AI 