# Importance-aware Co-teaching for Offline Model-based Optimization

arXiv: https://arxiv.org/abs/2309.11600 · HTML: https://mlsanity.com/paper/2309.11600/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2023.

## Built on by (most cited first, 28 of 28)

- [Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints](https://mlsanity.com/paper/2402.18012/) (2024)
- [Textual Knowledge Matters: Cross-Modality Co-Teaching for Generalized Visual Class Discovery](https://mlsanity.com/paper/2403.07369/) (2024)
- [ParetoFlow: Guided Flows in Multi-Objective Optimization](https://mlsanity.com/paper/2412.03718/) (2024)
- [Guided Trajectory Generation with Diffusion Models for Offline Model-based Optimization](https://mlsanity.com/paper/2407.01624/) (2024)
- [Offline Model-Based Optimization: Comprehensive Review](https://mlsanity.com/paper/2503.17286/) (2025)
- [Offline Multi-Objective Optimization](https://mlsanity.com/paper/2406.03722/) (2024)
- [Improved Off-policy Reinforcement Learning in Biological Sequence Design](https://mlsanity.com/paper/2410.04461/) (2024)
- [Offline Model-Based Optimization by Learning to Rank](https://mlsanity.com/paper/2410.11502/) (2024)
- [Preference-Guided Diffusion for Multi-Objective Offline Optimization](https://mlsanity.com/paper/2503.17299/) (2025)
- [Design Editing for Offline Model-based Optimization](https://mlsanity.com/paper/2405.13964/) (2024)
- ["Noisier" Noise Contrastive Eestimation is (Almost) Maximum Likelihood](https://mlsanity.com/paper/2405.16730/) (2024)
- [Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings](https://mlsanity.com/paper/2506.07109/) (2025)
- [Boosting Offline Optimizers with Surrogate Sensitivity](https://mlsanity.com/paper/2503.04181/) (2025)
- [Incorporating Surrogate Gradient Norm to Improve Offline Optimization Techniques](https://mlsanity.com/paper/2503.04242/) (2025)
- [ROOT: Rethinking Offline Optimization as Distributional Translation via Probabilistic Bridge](https://mlsanity.com/paper/2509.16300/) (2025)
- [LLM-TS Integrator: Integrating LLM for Enhanced Time Series Modeling](https://mlsanity.com/paper/2410.16489/) (2024)
- [Improving Controller Generalization with Dimensionless Markov Decision Processes](https://mlsanity.com/paper/2504.10006/) (2025)
- [SPREAD: Sampling-based Pareto front Refinement via Efficient Adaptive Diffusion](https://mlsanity.com/paper/2509.21058/) (2025)
- [Diffusion Large Language Models for Black-Box Optimization](https://mlsanity.com/paper/2601.14446/) (2026)
- [Pareto-Conditioned Diffusion Models for Offline Multi-Objective Optimization](https://mlsanity.com/paper/2602.00737/) (2026)
- [On the Learnability of Offline Model-Based Optimization: A Ranking Perspective](https://mlsanity.com/paper/2603.04000/) (2026)
- [AffinityFlow: Guided Flows for Antibody Affinity Maturation](https://mlsanity.com/paper/2502.10365/) (2025)
- [Learning Design-Score Manifold to Guide Diffusion Models for Offline Optimization](https://mlsanity.com/paper/2506.05680/) (2025)
- [Support-Proximity Augmented Diffusion Estimation for Offline Black-Box Optimization](https://mlsanity.com/paper/2605.11246/) (2026)
- [The Offline-Frontier Shift: Diagnosing Distributional Limits in Generative Multi-Objective Optimization](https://mlsanity.com/paper/2602.11126/) (2026)
- [Black-Box Optimization From Small Offline Datasets via Meta Learning with Synthetic Tasks](https://mlsanity.com/paper/2604.12325/) (2026)
- [Rethinking Learnability in Offline Data-driven Optimization](https://mlsanity.com/paper/2609.01493/) (2026)
- [ParetoTransport: Generative Optimization by Mass Transport Toward The Pareto Front](https://mlsanity.com/paper/2609.07706/) (2026)

## Abstract (arXiv)

Offline model-based optimization aims to find a design that maximizes a property of interest using only an offline dataset, with applications in robot, protein, and molecule design, among others. A prevalent approach is gradient ascent, where a proxy model is trained on the offline dataset and then used to optimize the design. This method suffers from an out-of-distribution issue, where the proxy is not accurate for unseen designs. To mitigate this issue, we explore using a pseudo-labeler to generate valuable data for fine-tuning the proxy. Specifically, we propose textitImportance-aware Co-Teaching for Offline Model-based Optimization~(ICT). This method maintains three symmetric proxies with their mean ensemble as the final proxy, and comprises two steps. The first step is textitpseudo-label-driven co-teaching. In this step, one proxy is iteratively selected as the pseudo-labeler for designs near the current optimization point, generating pseudo-labeled data. Subsequently, a co-teaching process identifies small-loss samples as valuable data and exchanges them between the other two proxies for fine-tuning, promoting knowledge transfer. This procedure is repeated three times, with a different proxy chosen as the pseudo-labeler each time, ultimately enhancing the ensemble performance. To further improve accuracy of pseudo-labels, we perform a secondary step of textitmeta-learning-based sample reweighting, which assigns importance weights to samples in the pseudo-labeled dataset and updates them via meta-learning. ICT achieves state-of-the-art results across multiple design-bench tasks, achieving the best mean rank of 3.1 and median rank of 2, among 15 methods. Our source code can be found here.