# One Big Net For Everything

arXiv: https://arxiv.org/abs/1802.08864 · HTML: https://mlsanity.com/paper/1802.08864/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2018.

## Built on by (most cited first, 16 of 16)

- [A Generalist Agent](https://mlsanity.com/paper/2205.06175/) (2022)
- [An Embodied Generalist Agent in 3D World](https://mlsanity.com/paper/2311.12871/) (2023)
- [On the link between conscious function and general intelligence in humans and machines](https://mlsanity.com/paper/2204.05133/) (2022)
- [Beyond Outlining: Heterogeneous Recursive Planning for Adaptive Long-form Writing with Language Models](https://mlsanity.com/paper/2503.08275/) (2025)
- [Guiding Online Reinforcement Learning with Action-Free Offline Pretraining](https://mlsanity.com/paper/2301.12876/) (2023)
- [Learning to Identify Critical States for Reinforcement Learning from Videos](https://mlsanity.com/paper/2308.07795/) (2023)
- [On Realization of Intelligent Decision-Making in the Real World: A Foundation Decision Model Perspective](https://mlsanity.com/paper/2212.12669/) (2022)
- [Metalearning Continual Learning Algorithms](https://mlsanity.com/paper/2312.00276/) (2023)
- [Learning One Abstract Bit at a Time Through Self-Invented Experiments Encoded as Neural Networks](https://mlsanity.com/paper/2212.14374/) (2022)
- [Habits and goals in synergy: a variational Bayesian framework for behavior](https://mlsanity.com/paper/2304.05008/) (2023)
- [Multi-Modal Cognitive Maps based on Neural Networks trained on Successor Representations](https://mlsanity.com/paper/2401.01364/) (2023)
- [Neural Computers](https://mlsanity.com/paper/2604.06425/) (2026)
- [RigLSTM: Recurrent Independent Grid LSTM for Generalizable Sequence Learning](https://mlsanity.com/paper/2311.02123/) (2023)
- [RPRA: Predicting an LLM-Judge for Efficient but Performant Inference](https://mlsanity.com/paper/2604.12634/) (2026)
- [CORAL: Learning Amyloid Fibril Ligand Docking with Cooperative Binding Rewards](https://mlsanity.com/paper/2607.17412/) (2026)
- [JevGPT: Thinking Fast and Slow in Autonomous Research](https://mlsanity.com/paper/2610.18272/) (2026)

## Abstract (arXiv)

I apply recent work on "learning to think" (2015) and on PowerPlay (2011) to the incremental training of an increasingly general problem solver, continually learning to solve new tasks without forgetting previous skills. The problem solver is a single recurrent neural network (or similar general purpose computer) called ONE. ONE is unusual in the sense that it is trained in various ways, e.g., by black box optimization / reinforcement learning / artificial evolution as well as supervised / unsupervised learning. For example, ONE may learn through neuroevolution to control a robot through environment-changing actions, and learn through unsupervised gradient descent to predict future inputs and vector-valued reward signals as suggested in 1990. User-given tasks can be defined through extra goal-defining input patterns, also proposed in 1990. Suppose ONE has already learned many skills. Now a copy of ONE can be re-trained to learn a new skill, e.g., through neuroevolution without a teacher. Here it may profit from re-using previously learned subroutines, but it may also forget previous skills. Then ONE is retrained in PowerPlay style (2011) on stored input/output traces of (a) ONE's copy executing the new skill and (b) previous instances of ONE whose skills are still considered worth memorizing. Simultaneously, ONE is retrained on old traces (even those of unsuccessful trials) to become a better predictor, without additional expensive interaction with the enviroment. More and more control and prediction skills are thus collapsed into ONE, like in the chunker-automatizer system of the neural history compressor (1991). This forces ONE to relate partially analogous skills (with shared algorithmic information) to each other, creating common subroutines in form of shared subnetworks of ONE, to greatly speed up subsequent learning of additional, novel but algorithmically related skills.