Reward-weighted log-likelihood objectives outperform complex off-policy estimators in large action spaces because their optimization landscapes are much easier to navigate.
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Off-Policy Learning in Large Action Spaces: Optimization Matters More Than Estimation
Reward-weighted log-likelihood objectives outperform complex off-policy estimators in large action spaces because their optimization landscapes are much easier to navigate.