# Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning

arXiv: https://arxiv.org/abs/1702.03274 · HTML: https://mlsanity.com/paper/1702.03274/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2017.

## Built on by (most cited first, 30 of 32)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2104.00783/) (2021)
- [Are LLMs All You Need for Task-Oriented Dialogue?](https://mlsanity.com/paper/2304.06556/) (2023)
- [Multilingual Autoregressive Entity Linking](https://mlsanity.com/paper/2103.12528/) (2021)
- [Process-Supervised Reinforcement Learning for Code Generation](https://mlsanity.com/paper/2502.01715/) (2025)
- [End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions](https://mlsanity.com/paper/2311.09008/) (2023)
- [Reference-Centric Models for Grounded Collaborative Dialogue](https://mlsanity.com/paper/2109.05042/) (2021)
- [Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems](https://mlsanity.com/paper/2104.09088/) (2021)
- [Reinforcement Learning for Generative AI: A Survey](https://mlsanity.com/paper/2308.14328/) (2023)
- [CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation](https://mlsanity.com/paper/2211.11617/) (2022)
- [A Cooperative Memory Network for Personalized Task-oriented Dialogue Systems with Incomplete User Profiles](https://mlsanity.com/paper/2102.08322/) (2021)
- [Can I Be of Further Assistance? Using Unstructured Knowledge Access to Improve Task-oriented Conversational Modeling](https://mlsanity.com/paper/2106.09174/) (2021)
- [Faster Deep Reinforcement Learning with Slower Online Network](https://mlsanity.com/paper/2112.05848/) (2021)
- [Conversational Tree Search: A New Hybrid Dialog Task](https://mlsanity.com/paper/2303.10227/) (2023)
- [A Student-Teacher Architecture for Dialog Domain Adaptation under the Meta-Learning Setting](https://mlsanity.com/paper/2104.02689/) (2021)
- [A Modular Task-oriented Dialogue System Using a Neural Mixture-of-Experts](https://mlsanity.com/paper/1907.05346/) (2019)
- [Towards a Zero-Data, Controllable, Adaptive Dialog System](https://mlsanity.com/paper/2403.17582/) (2024)
- [Towards Zero and Few-shot Knowledge-seeking Turn Detection in Task-orientated Dialogue Systems](https://mlsanity.com/paper/2109.08820/) (2021)
- [SYNERGY: Building Task Bots at Scale Using Symbolic Knowledge and Machine Teaching](https://mlsanity.com/paper/2110.11514/) (2021)
- [Task-oriented Dialogue Systems: performance vs. quality-optima, a review](https://mlsanity.com/paper/2112.11176/) (2021)
- [Neuro-Symbolic Visual Dialog](https://mlsanity.com/paper/2208.10353/) (2022)
- [How to Evaluate Your Dialogue Models: A Review of Approaches](https://mlsanity.com/paper/2108.01369/) (2021)
- [Physics-informed Evolutionary Strategy based Control for Mitigating Delayed Voltage Recovery](https://mlsanity.com/paper/2111.14352/) (2021)
- [Prior Lessons of Incremental Dialogue and Robot Action Management for the Age of Language Models](https://mlsanity.com/paper/2501.00953/) (2025)
- [Training like Playing: A Reinforcement Learning And Knowledge Graph-based framework for building Automatic Consultation System in Medical Field](https://mlsanity.com/paper/2106.07502/) (2021)
- [Taming Continuous Posteriors for Latent Variational Dialogue Policies](https://mlsanity.com/paper/2205.07633/) (2022)
- [A Neural Conversation Generation Model via Equivalent Shared Memory Investigation](https://mlsanity.com/paper/2108.09164/) (2021)
- [Every time I fire a conversational designer, the performance of the dialog system goes down](https://mlsanity.com/paper/2109.13029/) (2021)
- [Reinforcement Learning of Multi-Domain Dialog Policies Via Action Embeddings](https://mlsanity.com/paper/2207.00468/) (2022)
- [DiQAD: A Benchmark Dataset for End-to-End Open-domain Dialogue Assessment](https://mlsanity.com/paper/2310.16319/) (2023)

## Abstract (arXiv)

End-to-end learning of recurrent neural networks (RNNs) is an attractive solution for dialog systems; however, current techniques are data-intensive and require thousands of dialogs to learn simple behaviors. We introduce Hybrid Code Networks (HCNs), which combine an RNN with domain-specific knowledge encoded as software and system action templates. Compared to existing end-to-end approaches, HCNs considerably reduce the amount of training data required, while retaining the key benefit of inferring a latent representation of dialog state. In addition, HCNs can be optimized with supervised learning, reinforcement learning, or a mixture of both. HCNs attain state-of-the-art performance on the bAbI dialog dataset, and outperform two commercially deployed customer-facing dialog systems.