arXiv:2606.18831v1 Announce Type: cross Abstract: Long-context reasoning is an essential capability for large language models, particularly when they are deployed as autonomous agents that must reason over lengthy trajectories. Reinforcement learning (RL) has recently emerged as a dominant paradigm for improving this ability, yet existing work largely focuses on reward engineering while diverse training data remains scarce. We revisit this problem from a data-centric perspective and show that a simple yet effective data recipe alone, paired with a minimal outcome-based GRPO setup, suffices to
The rapid advancement of large language models and their deployment as autonomous agents necessitates improved long-context reasoning capabilities, which current RL methods struggle with using traditional reward engineering.
This research suggests a more scalable and data-centric approach to improve AI agent performance in complex, multi-step tasks, reducing reliance on labor-intensive reward engineering.
The focus for developing sophisticated AI agents shifts from complex reward function design to more efficient data curation and simple outcome-based reinforcement learning setups.
- · AI researchers focusing on data-centric approaches
- · Developers of autonomous AI agents
- · Cloud compute providers
- · AI researchers focused primarily on complex reward engineering
AI agents become more capable of reasoning over extended periods and handling complex, multi-turn tasks effectively.
This improved long-context reasoning enables the deployment of more reliable and versatile AI agents across various industries.
The reduced barrier to developing capable agents could accelerate the automation of white-collar workflows, leading to significant productivity gains and job market shifts.
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Read at arXiv cs.AI