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Existence and mechanisms of a reinforcement-learning ‘cross point’ after strong cold starts

Determine whether there exists a cross point at which, after a sufficiently strong supervised fine-tuning cold start, subsequent reinforcement learning yields no additional performance improvement for data-analytic agents, and, if such a point exists, characterize the fundamental mechanisms—such as policy-space saturation, diminishing exploratory signal, or reward-model limitations—that render reinforcement learning ineffective.

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Background

Through cold-start experiments, the authors observe diminishing marginal gains from RL as the strength of the SFT initialization increases and hypothesize that a threshold may exist beyond which RL provides no further improvement.

They explicitly pose as an open question both the existence of such a ‘cross point’ and the underlying causes that would make RL ineffective, suggesting potential mechanisms like policy-space saturation, weakened exploration signals, or limitations of reward models.

References

Setting aside overfitting, the current trend suggests that a cross point may emerge in which a sufficiently strong cold start leaves no room for further improvement via RL. Whether such a point truly exists, and, if it does, what fundamental mechanisms (e.g., saturation of the policy space, diminishing exploratory signal, or intrinsic limitations of the reward model) render further RL ineffective, constitutes an important open question for future work.

— Scaling Generalist Data-Analytic Agents (2509.25084 - Qiao et al., 29 Sep 2025) in Section 5 Analysis, subsection ‘RL can narrow the performance gap between different base models, but can hardly reverse the order’