A data-centric approach improves long-context reasoning in large language models, using eight curated datasets with 14K examples across retrieval, multi-evidence synthesis, and reasoning tasks. When paired with minimal outcome-based GRPO training, it achieves average gains of +7.2 to +6.4 points on seven benchmarks, outperforming prior RL training sets, and enhances agentic performance by +4.8 and +7.0 points on GAIA and BrowseComp respectively.
arxiv
arXiv cs.CL
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79d ago
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research
Data Recipe Boosts Long-Context Reasoning in LLMs
from English
Importance 3/3
Beats a top-lab benchmark
arXiv cs.CL
Alibaba (Qwen)
AI agents
Reasoning models
Training data
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