Qwen 3.5 trapped in thinking loops

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🦙Read original on Reddit r/LocalLLaMA

#model-flaw #thinking-loops #qwenqwen-3.5

💡Qwen 3.5's loop bug could derail your reasoning pipelines—know the flaw now.

⚡ 30-Second TL;DR

What Changed

Qwen 3.5 repeatedly echoes 'thinking loops' in responses

Why It Matters

Highlights reliability issues in Qwen 3.5 for chain-of-thought prompting, potentially affecting users in reasoning-heavy tasks.

What To Do Next

Test Qwen 3.5 with long chain-of-thought prompts to reproduce thinking loops.

Who should care:Developers & AI Engineers

🧠 Deep Insight

AI-generated analysis for this event.

🔑 Enhanced Key Takeaways

•The 'thinking loop' phenomenon in Qwen 3.5 is frequently attributed to the model's chain-of-thought (CoT) reasoning process failing to reach a termination condition, causing it to recursively re-evaluate its own internal logic.
•Community analysis suggests that these loops are exacerbated by specific system prompts or high temperature settings, which can destabilize the model's ability to finalize its reasoning chain.
•Users have reported that mitigating these loops often requires manual intervention, such as adjusting the 'stop tokens' or forcing a context window reset, as the model lacks an inherent self-correction mechanism for these specific recursive states.

📊 Competitor Analysis▸ Show

Feature	Qwen 3.5	DeepSeek-R1	OpenAI o3
Reasoning Architecture	Proprietary CoT	Open-weights CoT	Proprietary CoT
Loop Mitigation	Manual/Prompt-based	RL-based training	RL-based training
Licensing	Open Weights	Open Weights	Closed API

🛠️ Technical Deep Dive

Architecture: Qwen 3.5 utilizes a Mixture-of-Experts (MoE) backbone combined with a specialized reasoning head designed for multi-step logical deduction.
Reasoning Mechanism: The model employs an explicit 'thought' token block that is processed before the final response generation; loops occur when the model fails to generate the <|end_thought|> delimiter.
Training Data: The model was fine-tuned on synthetic reasoning datasets, which researchers suspect may contain 'poisoned' or repetitive sequences that trigger these loops during inference.

🔮 Future ImplicationsAI analysis grounded in cited sources

Future Qwen iterations will implement a hard-coded 'reasoning depth' limit.

To prevent infinite recursion, developers are likely to introduce a mandatory token limit for the reasoning phase that forces a termination regardless of the model's internal state.

RLHF protocols will shift focus toward penalizing repetitive reasoning patterns.

Current training methods prioritize accuracy, but the prevalence of loops necessitates a new reward signal that explicitly discourages self-referential repetition.