AI signal, minus the noise.

The paper proposes Retrieval-Augmented Reinforcement Fine-Tuning (RA-RFT), a post-training framework that teaches language models to reason by analogy. It first trains a reasoning-aware retriever via gold-relevance distillation, so that contexts are ranked by expected reasoning benefit rather than semantic overlap. The policy model is then fine-tuned using reinforcement learning on retrieved analogous demonstrations under verifiable outcome rewards, enabling it to leverage reasoning traces. Analysis shows that reasoning-aware retrieval surfaces complementary solution strategies that provide distinct scaffolding per problem. On AIME 2025, RA-RFT improves average@32 accuracy over GRPO by 7.1 points for Qwen3-1.7B and 2.8 points for Qwen3-4B, demonstrating that reasoning-aware retrieval is an orthogonal improvement to reward design or training curricula.

Researchers evaluated an LLM pipeline on 76 published social and behavioral science studies with predefined claims. Excluding 7 studies where the LLM failed to produce a viable effect size estimate, the pipeline recovered original effect sizes within ±0.05 Cohen's d in 41% of the remaining studies. It reached the same qualitative conclusion as the original study in 96% of cases, outperforming human reanalysts who achieved 34% effect-size recovery and 74% conclusion agreement. These findings suggest LLMs can automate and scale reproducibility assessments, providing a foundation for systematic auditing of empirical results.

Large language model training data curation requires data attribution methods to identify how individual samples influence model outputs. Traditional influence functions, though effective, are too slow and memory-intensive for large-scale use. The paper proposes Influcoder, which distills gradient influence rankings from decoder models into a dedicated encoder. This yields a quick, cost-effective approach to influence-based data attribution at scale.