Key Methods for Optimizing the Accuracy of Complex Reasoning
Qwen3-235B-A22B-Thinking-2507 is designed for complex reasoning and its accuracy can be further enhanced by..:
- Enabling Thinking Mode: The model generates intermediate inference steps with tags by default, and this structured output facilitates verification of the correctness of the ideas
- Ensuring adequate context: It is recommended to maintain a context length of ≥131072 tokens to provide enough space for multi-step reasoning
- Adjusting the decoding strategyAvoid greedy decoding, recommend beam search or top-p sampling to reduce repetition and increase diversity.
- Step-by-step verification mechanism: For tasks such as mathematical proofs, the model can be required to output step-by-step and manually validate key steps.
- Integration of external tools: Validation of intermediate results through Qwen-Agent integration with tools such as calculators, code executors, etc.
When dealing with math problems, for example, the following cue word structure can be used:
'Please prove Fermat's Little Theorem step by step and pause at each critical step for me to confirm.'
This interaction significantly improves the reliability of complex reasoning.
This answer comes from the articleQwen3-235B-A22B-Thinking-2507: A large-scale language model to support complex reasoningThe
May not be reproduced without permission:AI productivity tools " How to improve the accuracy of Qwen3-235B-A22B-Thinking-2507 in complex reasoning tasks?
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