Firstly, by expanding diversity in creative data (strategy 1). This involves providing the model with a more diverse set of
creative data during the “ Associable Instruction Tuning” stage to enhance initial LoT capabilities. This diversity enables the
LLM to continuously generate novel data effectively even under various weakly-associated conditions;
Secondly, by ensuring diversity in the noun set S (strategy 2). This implies using a more diverse and effective set of nouns
in S for sampling weakly-associated conditions, thereby facilitating better associative capabilities in LLM and ensuring the
quality of newly generated data.

Figure 27. The impact of data diversity on CLoT performance. The baseline is Qwen-VL on I2T type Oogiri game. Left: For diversity of
Oogiri-GO . Right: For diversity of weakly-associated conditions in noun set S.
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