Transformers Provably Implement In-Context Reinforcement Learning with Policy Improvement
📰 ArXiv cs.AI
Transformers can implement in-context reinforcement learning with policy improvement, enabling models to learn from trajectory data without parameter updates.
Action Steps
- Implement a linear self-attention transformer block to perform in-context reinforcement learning
- Use explicit parameter constructions to enable policy-improvement methods like semi-gradient SARSA and actor-critic
- Design a teacher-mimicking training protocol to improve the model's performance
- Apply the transformer model to trajectory data to infer and execute learning algorithms
- Test the model's ability to improve policies using in-context reinforcement learning
Who Needs to Know This
Researchers and engineers working on reinforcement learning and transformer models can benefit from this knowledge to improve their models' ability to learn from data without requiring parameter updates.
Key Insight
💡 Transformers can implement policy-improvement methods via explicit parameter constructions, enabling in-context reinforcement learning.
Share This
🤖 Transformers can do in-context reinforcement learning with policy improvement! 📈
Key Takeaways
Transformers can implement in-context reinforcement learning with policy improvement, enabling models to learn from trajectory data without parameter updates.
Full Article
Title: Transformers Provably Implement In-Context Reinforcement Learning with Policy Improvement
Abstract:
arXiv:2605.05755v1 Announce Type: cross Abstract: We investigate the ability of transformers to perform in-context reinforcement learning (ICRL), where a model must infer and execute learning algorithms from trajectory data without parameter updates. We show that a linear self-attention transformer block can provably implement policy-improvement methods, including semi-gradient SARSA and actor-critic, via explicit parameter constructions. Beyond existence, we design a teacher-mimicking training
Abstract:
arXiv:2605.05755v1 Announce Type: cross Abstract: We investigate the ability of transformers to perform in-context reinforcement learning (ICRL), where a model must infer and execute learning algorithms from trajectory data without parameter updates. We show that a linear self-attention transformer block can provably implement policy-improvement methods, including semi-gradient SARSA and actor-critic, via explicit parameter constructions. Beyond existence, we design a teacher-mimicking training
DeepCamp AI