Energy redistribution sample re-integration
It sounds like you yet have to adjust the importance function of the Markov Chain according to the way you assign paths to each of the two estimators, path tracing and ERPT.
If you think about it in the framework of MIS, you have two estimators with binary weights (according to your threshold). Now, the simplest (but inefficient) option to combine them would be to always run ERPT and path tracing for all paths, applying the weight to the splats only (this weighting is what I understand you do by separating them into different framebuffers).
In your case, however, you only enter the ERPT chain for the paths that you want to further explore, therefore you do not actually enter the chain in a stationary distribution proportional to the measurement contribution. This breaks ERPT, as the startup bias removal no longer works correctly. To fix it, you need to additionally account for your path selection strategy in the importance function such that it matches the initial distribution of paths entering ERPT: The MIS weight of the ERPT estimator needs to become a part of its targeted distribution, preventing energy from running off into paths that would never actually trigger an ERPT run. Thus, you should be able to make the startup bias removal work with your path selection strategy.