# MT-OPSD

The work finds that instruction-based image editing models degrade rapidly under recursive multi-turn editing and attributes this to a train–test mismatch in the conditioning distribution, since models are trained on clean source images but must repeatedly condition on their own imperfect outputs at inference; it proposes MT-OPSD, which rolls out the current student under an identity instruction to produce self-generated states containing its own errors, then uses the same model conditioned on the clean source as a teacher to transfer clean-condition editing behavior onto those states via sparse velocity matching, without multi-turn annotations; it also builds LME-Bench of 100 ten-turn editing sessions, raising SR@10 to 0.38–0.52 and cutting CR@10 to at most 0.

The work finds that instruction-based image editing models degrade rapidly under recursive multi-turn editing and attributes this to a train–test mismatch in the conditioning distribution, since models are trained on clean source images but must repeatedly condition on their own imperfect outputs at inference; it proposes MT-OPSD, which rolls out the current student under an identity instruction to produce self-generated states containing its own errors, then uses the same model conditioned on the clean source as a teacher to transfer clean-condition editing behavior onto those states via sparse velocity matching, without multi-turn annotations; it also builds LME-Bench of 100 ten-turn editing sessions, raising SR@10 to 0.38–0.52 and cutting CR@10 to at most 0.