Optimization solutions
Three key steps to improve the quality of GPT-OSS reasoning:
- Setting up a high-intensity reasoning mode: Configure .with_reasoning_effort("high") in system messages to activate deep reasoning chains, especially for scenarios such as mathematical proofs, code generation, etc.
- Utilizing Harmony Format Output: Parsing the analysis channel through the openai_harmony library to obtain the complete inference process, combined with the final channel results for manual verification.
- toolchain integration::
- Integration of Python tools to perform numerical calculations (requires configuration of PythonTool Docker environment)
- Call browser tool to validate factual information (requires EXA_API_KEY)
It is recommended to synchronize and adjust max_new_tokens to 512 or more to ensure output integrity, in conjunction with temperature=1.0 to maintain creativity. Tests show that the accuracy of the model in solving complex math problems in high intensity mode improves by 37%.
This answer comes from the articleGPT-OSS: OpenAI's Open Source Big Model for Efficient ReasoningThe
May not be reproduced without permission:AI productivity tools " How to optimize the inference quality of GPT-OSS models for complex tasks?
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