Progress-Aware Distillation targets stagnation in small agent models
Original title: Progress-Aware Distillation for Mitigating Stagnation in Small Language Model Agents
Tests on eight models from 0.6B to 8B parameters show higher success rates and faster task completion.
The paper says distilled small language model agents stall in repeated failed strategies as reasoning-action traces grow longer. Progress-Aware Distillation detects that student-teacher gap and applies an adaptive penalty during training. Authors say the stall appears across small models and task domains. They report better success rates and task-completion efficiency on in-domain and out-of-domain benchmarks.
Unconfirmed: Reported gains come from the authors' experiments and are not independently confirmed.
- The method encodes the student-teacher stagnation gap as an adaptive margin.
- Experiments cover eight small models from 0.6B to 8B parameters.
- Authors say stagnation appears across diverse models and task domains.
From the original article
1 min read at huggingface.co
While LLM-based agent systems have demonstrated remarkable proficiency in reasoning and solving complex real-world tasks, they also come with substantial token consumption and inference latency, limiting their practical deployment.
By Yuanpu Cao, Saket Sathe, Hanyu Wang · huggingface.co
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