Can models recover knowledge with completely unrelated retraining tasks?
This explores whether knowledge a model seems to have lost can be brought back by training on tasks that have nothing to do with that knowledge — and the corpus suggests the answer hinges on a distinction between creating knowledge and merely switching it back on.
This reads as a question about recovery and reactivation: if a model's knowledge looks degraded, can retraining on an unrelated task restore it? The corpus doesn't tackle this head-on, but several notes converge on a surprising idea — much of what training does is *elicit* capability that's already latent, not install new content. If that's true, then the specific task you retrain on matters far less than you'd expect, and even an unrelated one could pull dormant knowledge back to the surface.
The sharpest evidence comes from work showing that training signal can be almost decoupled from content. Models trained on deliberately corrupted, semantically irrelevant reasoning traces perform as well as those trained on correct ones — sometimes generalizing *better* out of distribution Do reasoning traces need to be semantically correct?. The traces act as computational scaffolding, not as meaningful lessons. That's a direct hint that the *form* of training (engaging a capability) can matter more than its literal subject — which is exactly what 'unrelated retraining' would rely on. Reinforcing this, five independent mechanisms all turn out to elicit reasoning that base models *already* contain; post-training selects rather than creates it Do base models already contain hidden reasoning ability?. If the bottleneck is elicitation rather than acquisition, recovery-by-unrelated-task becomes plausible.
But there's a hard boundary here, and the corpus is blunt about it. You can only reactivate what's still in the weights. Prompt optimization can retrieve existing knowledge but cannot inject anything absent from training Can prompt optimization teach models knowledge they lack? — the same activate-don't-add ceiling, just at inference time. So 'recovery' only works if the knowledge was latent, not erased. And whether it gets erased depends heavily on *how* you train: direct fine-tuning corrupts knowledge storage in the lower layers, while decoding-time proxy-tuning leaves base weights untouched and actually surpasses fine-tuning on knowledge tasks Can decoding-time tuning preserve knowledge better than weight fine-tuning?. That reframes the question — unrelated retraining could either *recover* knowledge by re-eliciting it, or *destroy* more of it by overwriting the layers where it lives.
There's also a reason some knowledge survives retraining better than others. Reasoning draws on broad, transferable procedural knowledge spread across many documents, whereas factual recall depends on narrow, document-specific memorization Does procedural knowledge drive reasoning more than factual retrieval?. The procedural kind is diffuse and redundant — exactly the kind of thing an unrelated task might re-engage — while a specific memorized fact has no such backup. So 'can models recover knowledge' may not have one answer: procedural skill is recoverable through elicitation, brittle facts are not.
If you want the cleaner escape hatch, several notes point away from retraining entirely. The forgetting problem is most severe precisely *because* you're updating weights — so externalized skill libraries Can agents learn new skills without forgetting old ones?, memory-based adaptation with frozen parameters Can agents learn continuously from experience without updating weights?, and inference-time composition of expert vectors Can models dynamically activate expert skills at inference time? all sidestep recovery by never corrupting the original knowledge in the first place. The thing you didn't know you wanted to know: the real lever isn't which task you retrain on, but whether you touch the weights at all.
Sources 8 notes
Models trained on systematically irrelevant traces maintain solution accuracy and sometimes improve out-of-distribution generalization, suggesting traces function as computational scaffolding rather than meaningful reasoning steps.
Five independent mechanisms—RL steering, critique fine-tuning, decoding changes, SAE feature steering, and RLVR—all elicit reasoning already present in base model activations. Post-training selects rather than creates reasoning; the bottleneck is elicitation, not capability acquisition.
Prompting works entirely within a model's pre-existing training distribution and cannot supply domain knowledge absent from training data. This creates a hard ceiling: no prompt strategy can compensate for missing foundational knowledge, only reorganize what already exists.
Proxy-tuning closes 88-91% of the alignment gap while surpassing direct fine-tuning on knowledge tasks by leaving base model weights untouched. Direct fine-tuning corrupts knowledge storage in lower layers, whereas proxy-tuning applies distributional shifts that primarily affect reasoning and style.
Analysis of 5 million pretraining documents shows reasoning relies on broad, transferable procedural knowledge from diverse sources, unlike factual recall which depends on narrow, document-specific memorization of target facts.
VOYAGER demonstrates that storing executable skills in an embedding-indexed library and composing complex skills from simpler ones allows agents to learn continuously while avoiding the forgetting that occurs with weight-update-based methods. Environmental feedback refines skills while an automatic curriculum drives continual exploration.
AgentFly formalizes agent learning as a Memory-augmented MDP with three memory modules (case, subtask, tool) that enable credit assignment and policy improvement entirely through memory operations. The approach achieved 87.88% on GAIA validation without modifying LLM parameters.
Transformer2 demonstrates that tuning only singular values within weight matrices produces composable expert vectors that dynamically mix at inference without interference, outperforming LoRA with fewer parameters and enabling continual specialization.