RoCo-ACE: Rollout-Conditioned Online Distillation for Retention-Aware Knowledge Injection

Abstract:Knowledge injection updates pretrained MLLMs with new factual or domain-specific knowledge, but fitting full authoritative answers can cause drift in non-updated behavior. Online distillation mitigates this drift by training on model-generated rollouts, yet uniform reference-conditioned distillation provides coarse supervision: it can under-emphasize reference-supported rollout tokens and supervise omitted facts only indirectly. We introduce RoCo-ACE, a rollout-conditioned online distillation objective for knowledge injection. RoCo uses same-rollout reference-free/reference-conditioned likelihood contrast to reallocate additional distillation weight to reference-supported rollout tokens, while ACE adds sparse reference-side anchored correction for authoritative anchors omitted from the rollout without full-answer imitation. Across three knowledge-injection settings, six retention benchmarks, multiple baselines, and multiple base models, RoCo-ACE achieves the best injected-knowledge accuracy among compared methods while keeping evaluated retention close to the base model.

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

arXiv:2607.24771 [cs.AI]

(or arXiv:2607.24771v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.24771 Focus to learn more arXiv-issued DOI via DataCite

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