Quantization effects on tool-failure recovery depend on prompts and evaluation methods
Quantization Effects on Tool-Failure Recovery Vary Across Prompts and Evaluation Designs
Artificial IntelligenceComputation and LanguageMachine Learning
Summary
Using simpler versions of language models can save cost but change how well these models recover from temporary tool failures. The authors found that the impact of this simplification varies depending on the specific prompt used and how success is measured. Sometimes simpler models seem better, sometimes worse, and different ways of scoring outputs can reverse which model is preferred. This means one set of tests or prompts isn’t enough to understand how robust a model is after quantization.
What this means in practice
- •For machine learning engineers: Choose quantized language model versions and evaluation methods carefully to ensure robust tool-failure recovery in deployed AI agents.
- •For ai system testers: Design evaluation protocols that test models across multiple prompts, scoring methods, and matching tasks for stable performance comparison.
Authors
Yuhe Hu
Abstract
Post-training quantization reduces the cost of deploying language-model agents, but its effect on recovery from temporary tool failures can depend on how recovery is evaluated. We compare 8-bit and 4-bit variants of Llama-3.1-8B-Instruct and Qwen2.5-7B-Instruct on twenty deterministic tool-use tasks and five prompts. The 8-bit-4-bit recovery comparison changes direction across prompts and evaluation targets. On tasks that both variants complete without faults under the same prompt, the difference ranges from 0 to +20.2 percentage points for Llama and from -50.0 to +35.0 points for Qwen. Full-pipeline point estimates favor 8-bit Llama under all five prompts, whereas the Qwen comparison changes direction across prompts. The evaluation target can also reverse the result. For Llama under one prompt, scoring each variant only on its own clean-passing tasks favors 4-bit by 17.5 points; scoring the same tasks for both variants gives no difference, while scoring the full pipeline favors 8-bit by 28.3 points. Executor leniency is a third such choice. Rescoring the same logs with strict output parsing, which 8-bit Llama violates far more often than 4-bit Llama under that prompt, turns that +28.3 into -15.0 while leaving Qwen essentially unchanged. These findings show that one prompt, one screened task set, and one scoring policy do not establish a stable conclusion about quantized-agent robustness. Evaluations should compare variants on matched tasks, report full-pipeline success for deployment decisions, state the scoring policy, and quantify uncertainty across tasks rather than injected fault sites.