# TANGO: RNA Topology and Geometry Co-Design

arXiv: https://arxiv.org/abs/2610.20277 · HTML: https://mlsanity.com/paper/2610.20277/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 72 references that are in the map.
- First posted on arXiv 14 Oct 2026.

## Builds on (most cited first, 24 of 24)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [Learning from Protein Structure with Geometric Vector Perceptrons](https://mlsanity.com/paper/2009.01411/) (2020)
- [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021)
- [A Survey of Multi-Objective Sequential Decision-Making](https://mlsanity.com/paper/1402.0590/) (2014)
- [The Complexity of Decentralized Control of Markov Decision Processes](https://mlsanity.com/paper/1301.3836/) (2013)
- [A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation](https://mlsanity.com/paper/1908.08342/) (2019)
- [RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks](https://mlsanity.com/paper/2403.00043/) (2024)
- [Learning to Design RNA](https://mlsanity.com/paper/1812.11951/) (2018)
- [gRNAde: Geometric Deep Learning for 3D RNA inverse design](https://mlsanity.com/paper/2305.14749/) (2023)
- [Accurate RNA 3D structure prediction using a language model-based deep learning approach](https://mlsanity.com/paper/2207.01586/) (2022)
- [RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design](https://mlsanity.com/paper/2301.10774/) (2023)
- [MO-MIX: Multi-Objective Multi-Agent Cooperative Decision-Making With Deep Reinforcement Learning](https://mlsanity.com/paper/2603.00730/) (2026)
- [Human-in-the-Loop Policy Optimization for Preference-Based Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2401.02160/) (2024)
- [Bridging Sequence-Structure Alignment in RNA Foundation Models](https://mlsanity.com/paper/2407.11242/) (2024)
- [Augmenting Biological Fitness Prediction Benchmarks with Landscapes Features from GraphFLA](https://mlsanity.com/paper/2510.24826/) (2025)
- [RIDER: 3D RNA Inverse Design with Reinforcement Learning-Guided Diffusion](https://mlsanity.com/paper/2602.16548/) (2026)
- [PA2D-MORL: Pareto Ascent Directional Decomposition based Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2603.19579/) (2026)
- [Position: Genomic Model Research Must Move Beyond Anecdotal Evaluation of Interpretability Methods](https://mlsanity.com/paper/2606.07607/) (2026)
- [Beyond Monotonicity: Revisiting Factorization Principles in Multi-Agent Q-Learning](https://mlsanity.com/paper/2511.09792/) (2025)

## Abstract (arXiv)

Designing RNA sequences that satisfy target tertiary-structure objectives is an important problem in RNA engineering, with broad applications in synthetic biology and therapeutics. Existing 3D RNA design methods have made substantial progress, but they largely treat the target fold as a geometry-only objective. This is incomplete for RNA, whose functional folds are specified not only by tertiary geometry but also by secondary base-pairing topology. We therefore formulate RNA inverse design as a topology-and-geometry co-design problem and propose TANGO, a multi-objective multi-agent reinforcement learning framework for this task. TANGO adopts a staged curriculum: it first learns a topology-aware policy from secondary-structure targets, then transfers this policy to tertiary-structure targets to train a general co-design model, and finally applies frontier-guided fine-tuning to refine target-specific trade-offs among secondary-structure fidelity, tertiary-structure fidelity, and sequence diversity. Experiments show that TANGO generates diverse and novel designs, improving tertiary- and secondary-structure performance by 18.3% and 52.7%, respectively, over the strongest baseline.