Agentic Long-Horizon RL: Weighing Batch Size and Gradient Update Strategies
ShikharMurty · x · 2026-08-08
The author initiated a technical discussion on training strategies for agentic long-horizon RL, outlining three main approaches and asking the community for their preferences:
- Small batch size with frequent gradient updates: Focuses on rapid iteration.
- Large batch size with fewer updates: Prioritizes stable gradient direction estimation.
- Small batch size combined with variance reduction techniques: Aims to control gradient noise while maintaining update frequency.
- Alternative methods.
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