AI

Base Models Can Reason By Taking a Cue From Training Data

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 06/10/2026, 04:18:25 UTC.

Briefings

This study demonstrates that base models can reason effectively by adopting specific training data cues, such as 'Okay' or 'Alright', which significantly improve math and coding performance. The research uses causal interventions to show these cues are learned from training data rather than being learned from scratch, and reveals how different cues correlate with safety behaviors.

Key points

• Fixing starting token cues like '.\n\nOkay' raises Olmo-3-7B's MATH-500 pass@1 accuracy from 42% to 78%.
• The cue 'Alright,' improves Qwen3-14B's performance from 72% to 87% on the same benchmark.

Context

Researchers investigate how training data associations influence reasoning behavior in base models, using causal data interventions to isolate learned cues from random noise.

Limitations

Evidence is limited to specific math and coding benchmarks; causal interventions were not performed on safety refusal behaviors.

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