Can Newer Models’ Gains Be Backported to Older Language Models?

Organizations keep a deployed language model for its latency, license, or validated behavior long after a successor release solves tasks it cannot. Distillation imitates a teacher wholesale, and model evolution migrates assets forward. Neither moves one verified gain backward into a model that must stay in service. A newer release fixes some of its predecessor’s mistakes but makes new ones of its own. A backport should inherit the fixes, verified task by task, and none of the new mistakes. CBack tests whether that is possible, first refusing version gaps indistinguishable from zero. Each verifier-rejected predecessor trajectory is then paired with a verifier-accepted repair, a version event. Preservation is normalized by the predecessor’s own resampling churn, and cost separates successor queries from a model’s own tokens. Of eight audited scopes in two model lineages, four carry a resolvable gain. All four recover, only one under successor guidance, and none needs successor access. Failure-gated querying instead yields 3.2× more admissible repairs per successor token than uniform querying. On GSM8K, two successor-free arms learn only from the predecessor’s verified samples, and only the one that also trains against its own failures recovers on a sealed test split. We conclude that failure-aware contrast improves recovery and the repair’s source decides how far. With its own repairs and no successor query, the deployed 1.5B model recovers 0.575 of that held-out gap and flips fewer of its successes than resampling does.