RL + Transformer = A General-Purpose Problem Solver
What if artificial intelligence could not only solve problems for which it
was trained but also learn to teach itself to solve new problems (i.e.,
meta-learn)? In this study, we demonstrate that a pre-trained transformer
fine-tuned with reinforcement learning over multiple episodes develops the
ability to solve problems that it has never encountered before - an emergent
ability called In-Context Reinforcement Learning (ICRL). This powerful
meta-learner not only excels in solving unseen in-distribution environments
with remarkable sample efficiency, but also shows strong performance in
out-of-distribution environments. In addition, we show that it exhibits
robustness to the quality of its training data, seamlessly stitches together
behaviors from its context, and adapts to non-stationary environments. These
behaviors demonstrate that an RL-trained transformer can iteratively improve
upon its own solutions, making it an excellent general-purpose problem solver.
67979f107dbf69e4e34cc51a
Micah Rentschler
extracted_confirmed
2025-02-06T17:21:33.919Z
66fffe1b3ec4cc293d40f2d5
https://cdn-avatars.huggingface.co/v1/production/uploads/66fffe1b3ec4cc293d40f2d5/-6Ff7rSQ_fvqBmTQRTlB4.png
Micah Rentschler
user
micahr234
67979f107dbf69e4e34cc51b
Jesse Roberts
admin_assigned
2025-01-27T15:09:51.776Z
63c19eb3a0ffa3857eae2efa
https://cdn-avatars.huggingface.co/v1/production/uploads/63c19eb3a0ffa3857eae2efa/pB-CbPa1QFTImiHanNCxU.jpeg
Jesse Roberts
user
JesseTNRoberts
RL + Transformer = A General-Purpose Problem Solver
What if artificial intelligence could not only solve problems for which it
was trained but also learn to teach itself to solve new problems (i.e.,
meta-learn)? In this study, we demonstrate that a pre-trained transformer
fine-tuned with reinforcement learning over multiple episodes develops the
ability to solve problems that it has never encountered before - an emergent
ability called In-Context Reinforcement Learning (ICRL). This powerful
meta-learner not only excels in solving unseen in-distribution environments
with remarkable sample efficiency, but also shows strong performance in
out-of-distribution environments. In addition, we show that it exhibits
robustness to the quality of its training data, seamlessly stitches together
behaviors from its context, and adapts to non-stationary environments. These
behaviors demonstrate that an RL-trained transformer can iteratively improve
upon its own solutions, making it an excellent general-purpose problem solver.
A pre-trained transformer fine-tuned with reinforcement learning develops the ability to solve unseen problems with sample efficiency and adaptability, demonstrating robust meta-learning.
transformer reinforcement learning In-Context Reinforcement Learning (ICRL) meta-learner sample efficiency out-of-distribution robustness behavior stitching non-stationary environments