Teaching Language Models to Forecast Research Success Through Comparative Idea Evaluation
This study explores whether language models can predict the success of research ideas before experiments. Using a dataset of 11,488 idea pairs, fine-tuned 8B models achieve 77.1% accuracy, outperforming GPT-5. RLVR further improves interpretability, demonstrating the potential of small models for autonomous scientific discovery.
[2605.21491] Teaching Language Models to Forecast Research Success Through Comparative Idea Evaluation
[Submitted on 6 Apr 2026]
Title:Teaching Language Models to Forecast Research Success Through Comparative Idea Evaluation
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Abstract:As language models accelerate scientific research by automating hypothesis generation and implementation, a new bottleneck emerges: evaluating and filtering hundreds of AI-generated ideas without exhaustive experimentation. We ask whether LMs can learn to forecast the empirical success of research ideas before any experiments are run. We study comparative empirical forecasting: given a benchmark-specific research goal and two candidate ideas, predict which will achieve better benchmark performance. We construct a dataset of 11,488 idea pairs grounded in objective outcomes from PapersWithCode. While off-the-shelf 8B-parameter models struggle (30% acc.), SFT dramatically boosts performance to 77.1%, outperforming GPT-5 (61.1%). By framing evaluation as a reasoning task via Reinforcement Learning with Verifiable Rewards (RLVR), we train models to discover latent reasoning paths, achieving 71.35% acc. with interpretable justifications. Through additional ablations and out-of-distribution tests, we show robustness to surface-level heuristics and transfer to both a cross-domain time-split test set and an independently constructed test set. Our results demonstrate that compute-efficient small language models can serve as effective, objective verifiers, offering a scalable path for autonomous scientific discovery.
Comments: ACL 2026 Findings
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as: arXiv:2605.21491 [cs.LG]
(or arXiv:2605.21491v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.21491
arXiv-issued DOI via DataCite
Submission history
From: Aniketh Garikaparthi [view email] [v1] Mon, 6 Apr 2026 19:03:11 UTC (5,610 KB)
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