Softmax Transformers implement in-context RL through equivalence to weighted softmax TD updates, with error decay under contraction and parameters as global minimizers of pretraining loss.
arXiv preprint arXiv:2307.11046 , title =
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With specific linear Transformer parameters, CoT generation equals iterative TD updates, yielding geometric error decay with CoT length until a context-length statistical floor, and those parameters globally minimize the pretraining loss.
Prospective Learning with Control proves ERM asymptotically achieves the Bayes optimal policy in non-stationary reset-free settings and outperforms time-aware RL on a 1D foraging benchmark.
LIFE is a proposed agentic framework that combines four components to enable incremental, flexible, and energy-efficient continual learning for HPC operations such as latency spike mitigation.
citing papers explorer
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Beyond Linear Attention: Softmax Transformers Implement In-Context Reinforcement Learning
Softmax Transformers implement in-context RL through equivalence to weighted softmax TD updates, with error decay under contraction and parameters as global minimizers of pretraining loss.
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Convergence and Emergence of In-Context Reinforcement Learning with Chain of Thought
With specific linear Transformer parameters, CoT generation equals iterative TD updates, yielding geometric error decay with CoT length until a context-length statistical floor, and those parameters globally minimize the pretraining loss.
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Optimal control of the future via prospective learning with control
Prospective Learning with Control proves ERM asymptotically achieves the Bayes optimal policy in non-stationary reset-free settings and outperforms time-aware RL on a 1D foraging benchmark.
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LIFE -- an energy efficient advanced continual learning agentic AI framework for frontier systems
LIFE is a proposed agentic framework that combines four components to enable incremental, flexible, and energy-efficient continual learning for HPC operations such as latency spike mitigation.