SPIN Unprocessed October 28, 2026 ai_technology research
When Does Pre-Trained Capacity Help Causal Learning? From Scalar Prediction to Counterfactual Post-Training
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arXiv:2610.33311v1 Announce Type: new Abstract: How does pre-trained model capacity relate to performance after adaptation for causal tasks? We study two systems built on the Qwen 2.5 family: CausalLLM, which estimates scalar potential outcomes with treatment-specific heads, and CausalGen, which generates treatment-conditioned text or code using soft prefixes and factual supervised fine-tuning (SFT), counterfactual SFT, and preference alignment. Increasing backbone size from 0.5B to 1.5B yields
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