Long-Zero: Self-Evolving Long-Context Reasoning via Information Degradation

Long-context language models can ingest increasingly long documents, yet post-training them for evidence-sensitive reasoning remains difficult because open-ended tasks rarely provide automatic verifiers or scalable answer annotations. We introduce Long-Zero, a self-evolving reinforcement learning framework that converts this supervision bottleneck into response ranking induced by controlled information degradation. For each document–question pair, Long-Zero progressively removes query-relevant evidence, uses a frozen generator to produce answer candidates from the resulting context views, and trains a policy to recover their latent quality order under the full context. A machine-checkable Kendall-$\tau$ reward enables group-relative policy optimization, while a feedback controller adapts the information gap between views to the policy's ranking competence. Across LongBench, Long-Zero improves over the corresponding base model in all four scored model–decoding settings.