Quantization Effects on Tool-Failure Recovery Vary Across Prompts and Evaluation Designs
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.
ProofPaper ↗
Key points
- 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.
- Full-pipeline point estimates favor 8-bit Llama under all five prompts, whereas the Qwen comparison changes direction across prompts.
- These findings show that one prompt, one screened task set, and one scoring policy do not establish a stable conclusion about quantized-agent robustness.
Sources (1)
- [1]Quantization Effects on Tool-Failure Recovery Vary Across Prompts and Evaluation DesignsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 05:17 AM
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.
Extractive summary: sentences quoted from the sources.
Before this
- Sep 29, 2026NVIDIA/TensorRT-LLM v1.3.0rc29
- Sep 28, 2026Holo4: powering generalist computer-use agents
- Sep 22, 2026vllm-project/vllm v0.30.0
- Aug 10, 2026vllm-project/vllm v0.27.0
- Jun 29, 2026vllm-project/vllm v0.24.0
- Jun 15, 2026vllm-project/vllm v0.23.0