Presents a likelihood-based benchmark for equation-suffix prediction in technical papers with controls to detect shortcut vulnerabilities in model forecasts.
Reinforcement learning on pre-training data.arXiv preprint arXiv:2509.19249
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Experiments indicate RL applied early in pre-training often matches full SFT-then-RL performance, targeted data composition outweighs scale for RL success, and averaging RL and SFT objectives outperforms sequential or single methods.
PreRL applies reward-driven updates to P(y) in pre-train space, uses Negative Sample Reinforcement to prune bad reasoning paths and boost reflection, and combines with standard RL in Dual Space RL to outperform baselines on reasoning tasks.
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A survey compiling RL methods, challenges, data resources, and applications for enhancing reasoning in large language models and large reasoning models since DeepSeek-R1.
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