Contrastive Reasoning Path Optimization for Improving Logical Inference in Language Models
Keywords:
contrastive learning, reasoning path optimization, logical inference, large language models, process supervision, AI governance, socio-technical systems, deployment sustainabilityAbstract
Large language models have achieved remarkable fluency across a wide range of tasks, yet their performance on multi-step logical inference remains uneven, brittle, and difficult to audit. This paper develops contrastive reasoning path optimization as a system-level framework for improving logical inference in language models by supervising the structure of intermediate reasoning trajectories rather than only the correctness of final answers. The framework treats reasoning as a searchable space of candidate paths, pairs successful and unsuccessful trajectories under controlled perturbations, and trains models to discriminate between them while preserving calibration and interpretability. The paper situates this approach within the broader literature on chain-of-thought prompting, preference optimization, and process supervision, and then examines the architectural, infrastructural, and institutional conditions required for reliable deployment. Particular attention is given to structural trade-offs between latency, verification cost, and accuracy; to the design of trace repositories with provenance and contamination controls; to failure modes such as unfaithful rationales and reward overoptimization; and to questions of fairness, accountability, and sustainability in large-scale socio-technical systems. The analysis concludes that contrastive path optimization is best understood not as a single training trick but as an infrastructure commitment that couples data governance, evaluation science, and deployment economics. Without corresponding investment in auditability and policy oversight, gains in measured reasoning accuracy risk remaining fragile and unevenly distributed across populations and domains.
References
1. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824-24837.
2. Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large language models are zero-shot reasoners. Advances in Neural Information Processing Systems, 35, 22199-22213.
3. Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. (2023). Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems, 36, 11809-11822.
4. Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., & Zhou, D. (2023). Self-consistency improves chain of thought reasoning in language models. International Conference on Learning Representations.
5. Li, Q. (2026, May). TrajLite PPO: Scalable Reasoning Path Filtering and Alignment for Small Parameter Large Language Models. In 2026 2nd International Conference on Artificial Intelligence and Digital Ethics (ICAIDE) (pp. 29-32). IEEE.
6. Zelikman, E., Wu, Y., Mu, J., & Goodman, N. D. (2022). STaR: Bootstrapping reasoning with reasoning. Advances in Neural Information Processing Systems, 35, 15476-15488.
7. Advances in Neural Information Processing Systems, 35, 27730-27744.
8. Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., & Amodei, D. (2017). Deep reinforcement learning from human preferences. Advances in Neural Information Processing Systems, 30, 4299-4307.
9. Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C. D., & Finn, C. (2023). Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems, 36, 53728-53741.
10. Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
11. Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531.
12. Gao, L., Schulman, J., & Hilton, J. (2023). Scaling laws for reward model overoptimization. Proceedings of the 40th International Conference on Machine Learning, 202, 10835-10866.
13. Bai, Y., Jones, A., Ndousse, K., Askell, A., Chen, A., DasSarma, N., Drain, D., Fort, S., Ganguli, D., Henighan, T., Joseph, N., Kadavath, S., Kernion, J., Conerly, T., El-Showk, S., Elhage, N., Hatfield-Dodds, Z., Hernandez, D., Johnston, S., ... Kaplan, J. (2022). Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862.
14. Turpin, M., Michael, J., Perez, E., & Bowman, S. R. (2023). Language models don't always say what they think: Unfaithful explanations in chain-of-thought prompting. Advances in Neural Information Processing Systems, 36, 74952-74965.
15. Lanham, T., Chen, A., Radhakrishnan, A., Steiner, B., Denison, C., Hernandez, D., Li, D., Durmus, E., Hubinger, E., Kernion, J., Tran-Johnson, E., Nguyen, K., Cheng, N., Joseph, N., Schiefer, N., Rausch, O., Larson, R., McCandlish, S., Kundu, S., ... Bowman, S. R. (2023). Measuring faithfulness in chain-of-thought reasoning. arXiv preprint arXiv:2307.13702.
16. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610-623.
17. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J. Q., Demszky, D., ... Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
18. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3645-3650.
19. Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L. M., Rothchild, D., So, D., Texier, M., & Dean, J. (2021). Carbon emissions and large neural network training. arXiv preprint arXiv:2104.10350.
20. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency, 220-229.
21. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daume III, H., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86-92.
22. Leike, J., Krueger, D., Everitt, T., Martic, M., Maini, S., & Legg, S. (2018). Scalable agent alignment via reward modeling: A research direction. arXiv preprint arXiv:1811.07871.
23. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998-6008.
24. Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. D. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., ... Zaremba, W. (2021). Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374.
25. Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 33-44.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Advanced Artificial Intelligence Research
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
