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Life sciences · Preprint
arXiv · August 10, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint proposes TrajVal, a lightweight method to estimate task learnability—the responsiveness of a task to further RL training—before training begins, and reports that it improves data efficiency over uniform sampling on mathematical and logical reasoning benchmarks. The work addresses a practical problem in LLM post-training but lacks peer review, quantified effect sizes on held-out benchmarks, and clarity on sample sizes and statistical significance.
Comparative empirical study with benchmark evaluation. Large language models of multiple scales evaluated on mathematical and logical reasoning tasks. Intervention: TrajVal task-sampling prior based on estimated per-task learnability. Compared with: Uniform task sampling and existing online scheduling methods.
Task learnability is reproducible across independently sampled training contexts Learnability is predictive of downstream utility in mathematical and logical reasoning TrajVal improves data efficiency over uniform task sampling
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Early-stage empirical work proposing a new method for task selection in LLM post-training, demonstrated on benchmarks but without peer review, hard endpoints, or evidence of deployment impact.
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Reinforcement learning (RL) has become a central post-training paradigm for eliciting reasoning capabilities in large language models, yet uniform task sampling allocates compute without regard to differences in how tasks respond to optimization. Existing task-valuation methods mostly rely on snapshot-based signals such as current pass rate or reward, which estimate how solvable a task is under the current policy. However, tasks with similar current solvability can still differ substantially in how positively they respond to further training. We study this residual axis as task learnability: a regime-conditional measure of expected positive response to continued training under a fixed RL post-training regime. By analyzing per-task reward trajectories, we find that learnability is reproducible across independently sampled training contexts and predictive of downstream utility. To make this signal practical before training begins, we propose TrajVal, a lightweight probe-based estimator that approximates per-task learnability from a short probe run and two endpoint evaluations. TrajVal can be used either as a standalone static prior for task sampling or as a multiplicative prior for existing online schedulers. Experiments on mathematical and logical reasoning benchmarks across multiple model scales show that TrajVal improves data efficiency over uniform sampling and provides complementary gains when combined with online scheduling methods.
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