Generalization of Progress-Aware Filtering

Determine whether Reasoning-Progress-Aware Reward Filtering for On-Policy Distillation (R$^2$-OPD) generalizes effectively to larger language models and diverse application domains.

Background

Reasoning-Progress-Aware Reward Filtering for On-Policy Distillation (R2^2-OPD) is evaluated on two small, reasoning-oriented student–teacher model configurations and mathematical benchmarks. Although the reported experiments show improvements over standard on-policy distillation, the conclusion explicitly leaves unresolved whether these gains extend to larger models and to domains beyond the evaluated mathematical reasoning tasks.

References

Future work will investigate approaches to reduce the cost and variance of process reward estimation and examine whether progress-aware filtering generalizes to larger models and other diverse domains.

— Beyond Imitation: Filtering On-Policy Distillation by Reasoning Progress  (2608.19408 - Yang et al., 19 Aug 2026) in Conclusion

Whether our findings extend to larger models or other domains remains to be studied.

— Gains and Collapse in On-Policy Distillation:A Reinforcement Learning Perspective  (2610.03185 - Cui et al., 2 Oct 2026) in Limitations section