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This paper identifies gradient oscillation and residual explosion as causes of training instability in Looped Transformers, and proposes Fully Looped Transformer with two parameter-free modifications (Fully Looped Architecture and Attention Injection) to stabilize training up to 12 loop iterations, achieving up to 13.2% improvement in downstream performance.