TFM-S3 uses a tabular foundation model to predict returns and guide intermittent global exploration within an SVD-derived policy subspace, yielding faster early convergence and better final performance than TD3 and population-based methods under fixed rollout budgets.
Continuous subspace optimization for continual learning
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Can Tabular Foundation Models Guide Exploration in Robot Policy Learning?
TFM-S3 uses a tabular foundation model to predict returns and guide intermittent global exploration within an SVD-derived policy subspace, yielding faster early convergence and better final performance than TD3 and population-based methods under fixed rollout budgets.