Fixing Qwen Repetition IMPROVEMENT
Adding fake tools like 'check_mars_pebble_movement' fixes a major reasoning flaw in the 72B parameter model.
The open-source AI community has uncovered a clever workaround for a persistent bug in Alibaba's Qwen 2.5 large language model. Users on forums like r/LocalLLaMA reported that the 72-billion-parameter model would often get stuck in repetitive reasoning loops, outputting the same 'thought' tokens multiple times before providing an answer. This significantly slowed down performance and degraded output quality for complex tasks.
Instead of modifying the model's architecture or retraining, a user discovered that the issue stems from Qwen's training on agentic scenarios. The model expects to call tools (like search or calculators) during reasoning. By providing a system prompt listing 10 intentionally useless tools—such as 'count_fictional_shoe_atoms' or 'adjust_fake_universe_gravity'—the model is tricked into avoiding fake tool calls. This simple prompt engineering hack reduces repetitive outputs by over 90%, allowing the model to reason more directly and efficiently without any code changes.
- Fixes repetitive 'thinking' loops in Alibaba's 72B parameter Qwen 2.5 model with a simple prompt hack
- Uses 10 fictional, useless tools (e.g., 'translate_to_16th_century_bee_dance') to trick the agent-trained architecture
- Reduces repetition by over 90% without modifying model weights, improving reasoning speed and output quality
Why It Matters
Enables developers to deploy Qwen more reliably for complex reasoning tasks without waiting for an official patch from Alibaba.