Perplexity-guided reasoning annotations scale pretraining data synthesis
Training data scarcity is now the binding constraint on LLM scaling, not compute. REER-PT tackles this by automatically annotating raw pretraining corpora with intermediate reasoning steps that bridge implicit logical gaps between context and continuation. The system uses perplexity as a signal to identify high-value augmentation targets, then filters for length and information leakage. This shifts the data-curation bottleneck from manual annotation to algorithmic synthesis, potentially unlocking orders of magnitude more usable training material from existing text. The approach matters because it reframes pretraining as a reasoning-reconstruction problem rather than pure next-token fitting, which could improve both sample efficiency and downstream reasoning capability.
Modelwire context
ExplainerREER-PT doesn't just generate synthetic reasoning steps; it uses perplexity as a filter to identify which gaps in raw text are worth annotating, then validates against information leakage. This targeting mechanism is what separates it from naive data synthesis.
This connects directly to the reasoning-efficiency work from late August. GMTS identified which tokens matter most during RL training; REER-PT tackles the upstream problem of which training examples deserve reasoning annotation in the first place. Together they suggest a two-stage optimization: first, curate pretraining data by identifying high-value reasoning gaps (REER-PT), then optimize how models learn from that curated set (GMTS). The Geometry of Divergence paper also shares a core concern: reasoning quality degrades under certain conditions. REER-PT's approach to embedding intermediate steps directly into pretraining could address representation stability earlier than post-hoc intervention.
If models trained on REER-PT-augmented data show measurable gains on chain-of-thought tasks without proportional gains on factual recall benchmarks, that confirms the method is genuinely improving reasoning rather than just padding training set size. Watch for ablations comparing perplexity-filtered augmentation against random augmentation on the same corpus size.
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MentionsREER-PT · Reverse-Engineered Reasoning
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