Empirical validation of theoretical results in realistic scenarios

Verify which parts of the theoretical convergence and off-policyness results for reward-guided self-training with the RE algorithm hold in realistic scenarios.

Background

The paper analyzes RE, a generalized REINFORCE procedure that fine-tunes a policy on self-generated, reward-weighted data while updating the rollout distribution every S gradient steps. Its formal results concern multi-armed bandits with softmax policies in the infinite-sample limit, including global convergence, convergence rates, and acceleration from suitable off-policyness.

The authors explicitly leave unresolved whether these theoretical findings remain valid in realistic applications beyond the idealized setting studied. This problem concerns empirical verification of the results in such scenarios, as well as identifying discrepancies between theoretical predictions and practical behavior.

References

In terms of empirical work, it remains open to verify which parts of our results hold true in realistic scenarios, see if our theoretical results can inspire better practice of reward-guided self-training, and identify gaps between theory and practice that require further research.

— Fine-Tuning on Self-Generated and Reward-Weighted Data: Learning Dynamics, Convergence Rates, and Benefits of Off-Policyness  (2609.36945 - Wang et al., 29 Sep 2026) in Section 6, “Limitations and future work”