reviews: 
Comments: The paper introduces Scaffolder, a model-based reinforcement learning approach that effectively utilizes privileged information during training. The core problem addresses learning an optimal unprivileged policy π- while leveraging rich privileged observations o+ that are unavailable at test time. Unlike prior work predominantly using model-free approaches, this paper adopts a model-based strategy. The method trains dual world models using DreamerV3 as the foundation—one for privileged observations (WM+) and one for target observations (WM-). A learned latent translator converts privileged latent states z+ into embeddings compatible with WM-, enabling knowledge transfer through the latent space. The privileged latent state z+ is also incorporated into the reward function R(z+,z-) to inform the critic. Additionally, a privileged policy π+ is trained for improved exploration, and a decoder reconstructs o+ from z-. This design is evaluated on 'sensory deprivation' tasks in robotic domains where specific sensors are designated as privileged. Comparisons include DreamerV3 baselines, single-model variants with privileged decoding, and model-free methods such as privileged information decay schedules, asymmetric actor-critic approaches, and behavioral cloning from privileged policies.
Strengths: The paper delivers a comprehensive treatment of prior work on leveraging privileged information and introduces a valuable experimental benchmark for this research area. The proposed enhancement to DreamerV3 demonstrates clear improvements over alternative world model configurations. Although Scaffolder incorporates multiple components, their contributions to improved world modeling and exploratory behavior are well-articulated. The comparison against established model-free baselines is thorough and demonstrates Scaffolder's superiority even when model-free methods receive substantially more environment interactions. The experimental suite spans diverse and compelling robotic tasks, with particularly noteworthy discoveries such as emergent spiral exploration patterns when facing observational constraints. The S3 benchmark itself represents a valuable resource for future research on privileged information in MDPs, independent of the algorithmic contributions.
Weaknesses: The approach necessitates training two distinct world models—one for z- and one for z+—effectively doubling the world modeling workload. While this is reasonably evaluated by environment interaction metrics rather than gradient computation, it remains a significant engineering complexity that could introduce training instabilities. The method compounds the typical actor-critic coordination challenge by introducing additional hyperparameter sensitivities: balancing learning rates between the privileged and unprivileged world models, synchronizing the privileged explorer π+ with the target policy π-, and preventing either from dominating the other's learning dynamics.
Recommendation: 10: strong accept, should be highlighted at the conference
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 3 good
Clarity: 4 excellent