Quantization Effects on Tool-Failure Recovery Vary Across Prompts and Evaluation Designs
Read the original on arXiv AI →The study investigates how post‑training quantization (8‑bit vs. 4‑bit) affects language‑model agents’ ability to recover from temporary tool failures. Using Llama‑3.1‑8B‑Instruct and Qwen2.5‑7B‑Instruct on twenty deterministic tool‑use tasks across five prompts, the authors find that recovery performance varies with prompt, evaluation target, and scoring policy, sometimes favoring 8‑bit, sometimes 4‑bit, and even reversing direction under different conditions. The results highlight that a single prompt, task set, or scoring rule cannot reliably determine the robustness of quantized agents. whyItMatters":"The findings emphasize that evaluation design choices critically influence conclusions about quantized‑agent robustness, underscoring the need for comprehensive, matched‑task assessments before deployment decisions are made."
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.