Rank Collapse Is Recoverable, Growing $|Q|$ Is Not: Out-of-Sample Early Warning for Value Divergence in High-UTD Soft Actor-Critic

Abstract:Raising the update-to-data (UTD) ratio breaks off-policy critics in two ways grouped as "plasticity loss": collapsing representations and growing value magnitude $|Q|$. We separate them in Soft Actor-Critic (SAC) with scaled critics (width 2048, no normalization). Collapse is survivable: at UTD ratio 16, HalfCheetah critics with most units dormant keep learning, and the training guard, which stops runs whose loss or $|Q|$ explodes, never flags them. Within one high-UTD SAC configuration, runs start close together, and how far a critic's $\log_{10}|Q|$ has climbed by step 15k, its early growth, ranks the runs by how soon the guard flags them. At 15k, a flagged run's $|Q|$ sits a median of over a hundredfold below its flag level, yet the climb's rate already orders the flags (Harrell's C and out-of-sample AUC 0.78 on Walker2d, 0.98 on Ant, at UTD ratio 4). Dormancy does not. Aborting on this rate saves about a tenth of held-out Walker2d compute and stays net-positive live. A LayerNorm critic lowers the rate, removes the flag on Walker2d at UTD ratio 4 and lowers the return.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2609.32819 [cs.AI]

(or arXiv:2609.32819v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.32819 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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