InterQ-RL: Interaction-Aware Correction of Execution Mismatch in Quantized Agentic RL
Read the original on arXiv Machine Learning →InterQ‑RL is a new method for multi‑turn agent reinforcement learning that addresses execution mismatch caused by low‑precision quantized rollouts. It corrects the mismatch by applying bounded execution‑prefix adjustments over the policy history, allocating correction strength across action segments within a fixed displacement budget, and does so without extra model forward passes. Experiments on ALFWorld, WebShop, and ScienceWorld with 4B‑30B backbones show that InterQ‑RL achieves the best quantized performance, matching BF16 within 0.7 points and surpassing it in two settings while maintaining or improving learning speed.
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