Training-time effects of the EvoResearcher reward decomposition

Determine whether training the EvoResearcher reward decomposition through the proposed GRPO objective causes the training-time version to inherit, amplify, or invert the effects observed for the inference-time protocol.

Background

EvoResearcher uses correctness, efficiency, reflection depth, and tool-call diversity as prompt-level mechanisms during inference, while also specifying these components as a GRPO training objective for an evolving virtual world. The paper validates only the frozen-backbone, inference-time protocol and does not execute the training blueprint. Consequently, it remains unresolved whether incorporating the same reward decomposition into model training will reproduce the observed behavior, strengthen it, or produce qualitatively different effects.

References

Whether the training-time version inherits, amplifies, or inverts the inference-time effects is an open empirical question that the protocol makes substantially cheaper to investigate, since the same reward decomposition drives both.

— Training-Free Inference-Time Self-Reflection and Cost-Bounded Early Stopping for Large Language Models  (2608.18884 - Yu et al., 19 Aug 2026) in Section 6, Discussion, subsection “Implications”