Reinforcement learning (RL) is an effective way to strengthen the long-context reasoning of large language models (LLMs), but it needs training data that are long, diverse, challenging, and verifiable
LongZero enables large language models to self-improve long-context reasoning by synthesizing structured multi-hop and retrieval tasks and training with GRPO without external data.
Reinforcement learning (RL) is an effective way to strengthen the long-context reasoning of large language models (LLMs), but it needs training data that are long, diverse, challenging, and verifiable. Human annotation of such data is expensive, naturally occurring data are too scarce to sustain RL, and existing synthesis methods depend on external corpora or stronger teacher models, which become harder to obtain as the trained models get stronger. Recently, RL from zero data, in which a model learns from tasks it proposes itself, has achieved excellent performance, but existing methods target short-context tasks. Therefore, we propose LongZero, a method that lets an LLM improve its own…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
LongZero: Enhancing Long-Context Reasoning in LLMs from Zero Data was first seen on Hugging Face · Daily papers on Oct 30, 2026. Since then it has appeared in 16 checks on 2 different days. Its best position was #25 of 30, reached on Oct 30, 07:28 UTC. It dropped off the list after Oct 31, 11:28 UTC.
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Not at the moment. It was last on the list on Oct 31, 11:28 UTC, after 2 days of trending.
#25 on Hugging Face · Daily papers, on Oct 30, 07:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Oct 30, 2026 and has been on the list on 2 days (16 checks) since.