Preference signals in LLM alignment are concentrated in early response tokens, so models trained on data truncated to the first half perform as well as or better than those trained on full responses.
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Shallow Preference Signals: Large Language Model Aligns Even Better with Truncated Data?
Preference signals in LLM alignment are concentrated in early response tokens, so models trained on data truncated to the first half perform as well as or better than those trained on full responses.