Soft compression stores a text span as k continuous compressed vectors for a frozen decoder, but existing compressors are usually tied to the decoder used during training. We study whether those vectors can be retargeted to a different frozen decoder through its input-embedding interface under a controlled reconstruction protocol. Our benchmark covers seven target decoders, three vector budgets, and directed source–target pairs, comparing Random, shared-embedding least squares (LS), Converter-only, Enc-Conv, and target-native Origin. Converter-only maps vectors from a frozen source compressor, whereas Enc-Conv also adapts the source-initialized vector generator. Transfer is feasible but decoder-conditioned: trainable retargeting can approach Origin BLEU on decoder-compatible targets, while LS is often close to Random despite using shared token anchors. Enc-Conv consistently matches or outperforms Converter-only, with its largest gains on difficult transfers. Selected-pair multi-seed runs and a small SQuAD probe provide complementary evidence, although downstream utility remains target-dependent. Perturbation and geometry probes are consistent with decoder-specific readable regions. Finally, pair-level readability scores provide weak-to-moderate pre-transfer screening signals.

Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks. However, it requires non-trivial efforts to implement these methods on different models. We present LlamaFactory, a unified framework that integrates a suite of cutting-edge efficient training methods. It provides a solution for flexibly customizing the fine-tuning of 100+ LLMs without the need for coding through the built-in web UI LlamaBoard. We empirically validate the efficiency and effectiveness of our framework on language modeling and text generation tasks. It has been released at https://github.com/hiyouga/LLaMA-Factory and received over 25,000 stars and 3,000 forks.