Truncated reasoning traces may help models handle mid-process interrupts
stochasticchasm · x · 2026-08-23
Discussion on the intent behind truncating reasoning traces in model training:
- Purpose: To expose the model to short reasoning without learning to emit /think mid-trace.
- Learned Behavior: The model learns to condition good responses on halfway truncated reasoning.
- Use Case: An inference engine could hard-inject /think when the thinking budget (e.g., 16k) runs out, and the model would remain stable due to SFT exposure.
Related event: Community debates why token budgets poorly model reasoning limits(3 posts)→
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