Stochastic approximation in non-markovian environments revisited
arXiv stat.ML / 3/24/2026
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Key Points
- The paper revisits stochastic approximation when the driving process is non-Markovian and additionally non-ergodic, expanding the theoretical setting beyond standard assumptions.
- It develops an analytic framework aimed at explaining transformer-based learning, with a focus on how the attention mechanism relates to learning dynamics.
- The framework is also positioned to inform continual learning, emphasizing that such methods may depend on the full history of data in principle.
- The work is presented as an arXiv preprint (v1) and builds on the author’s prior research on non-Markovian stochastic approximation.
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