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Supervised fine-tuning (SFT) and reinforcement learning (RL) are widely used post-training techniques for foundation models.
Algorithmic stability and generalization performance
Bousquet, O. and Elisseeff, A · 2000
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Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
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Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al · 2018
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Reinforcement Learning: An Introduction
Sutton, R. S. and Barto, A. G · 2018
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Reinforcement learning: Theory and algorithms
Agarwal, A., Jiang, N., Kakade, S. M., and Sun, W · 2019
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Fine-tuning language models from human preferences
Ziegler, D. M., Stiennon, N., Wu, J., Brown, T. B., Radford, A., Amodei, D., Christiano, P., and Irving, G · 2019
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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On the opportunities and risks of foundation models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al · 2021
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Training verifiers to solve math word problems
Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., et al · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
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Understanding deep learning (still) requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2021
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Quantifying memorization across neural language models
Carlini, N., Ippolito, D., Jagielski, M., Lee, K., Tramer, F., and Zhang, C · 2022
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Scaling instruction-finetuned language models
Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, E., Wang, X., Dehghani, M., Brahma, S., et al · 2022
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What images are more memorable to machines?
Han, J., Zhan, H., Hong, J., Fang, P., Li, H., Petersson, L., and Reid, I · 2022
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
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STaR: Bootstrapping reasoning with reasoning
Zelikman, E., Wu, Y., Mu, J., and Goodman, N · 2022
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Opt: Open pre-trained transformer language models
Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X. V., et al · 2022
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LMRL Gym: Benchmarks for multi-turn reinforcement learning with language models
Abdulhai, M., White, I., Snell, C., Sun, C., Hong, J., Zhai, Y., Xu, K., and Levine, S · 2023
Cited alongside, same era.
ShareGPT4V: Improving large multi-modal models with better captions
Chen, L., Li, J., Dong, X., Zhang, P., He, C., Wang, J., Zhao, F., and Lin, D · 2023
Cited alongside, same era.
AlphaZero-like tree-search can guide large language model decoding and training
Feng, X., Wan, Z., Wen, M., McAleer, S. M., Wen, Y., Zhang, W., and Wang, J · 2023
Cited alongside, same era.
Introducing Gemini: Our largest and most capable AI model, 2023
Google, D · 2023
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Training compute-optimal large language models
Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. d. L., Hendricks, L. A., Welbl, J., Clark, A., et al · 2023
Jaech, A., Kalai, A., Lerer, A., Richardson, A., El-Kishky, A., Low, A., Helyar, A., Madry, A., Beutel, A., Carney, A., et al · 2024
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Investigating data contamination for pre-training language models
Jiang, M., Liu, K. Z., Zhong, M., Schaeffer, R., Ouyang, S., Han, J., and Koyejo, S · 2024
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What do learning dynamics reveal about generalization in LLM reasoning?
Kang, K., Setlur, A., Ghosh, D., Steinhardt, J., Tomlin, C., Levine, S., and Kumar, A · 2024
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LLaVA-NeXT: Improved reasoning, ocr, and world knowledge, 2024
Liu, H., Li, C., Li, Y., Li, B., Zhang, Y., Shen, S., and Lee, Y. J · 2024
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Quantifying generalization complexity for large language models
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Cited alongside, same era.
Improved baselines with visual instruction tuning
Liu, H., Li, C., Li, Y., and Lee, Y. J · 2023
Cited alongside, same era.
MathVista: Evaluating mathematical reasoning of foundation models in visual contexts
Lu, P., Bansal, H., Xia, T., Liu, J., Li, C., Hajishirzi, H., Cheng, H., Chang, K.-W., Galley, M., and Gao, J · 2023
Cited alongside, same era.
Is reinforcement learning (not) for natural language processing: Benchmarks, baselines, and building blocks for natural language policy optimization
Ramamurthy, R., Ammanabrolu, P., Brantley, K., Hessel, J., Sifa, R., Bauckhage, C., Hajishirzi, H., and Choi, Y · 2023
Cited alongside, same era.
Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al · 2023
Cited alongside, same era.
ResMem: Learn what you can and memorize the rest
Yang, Z., Lukasik, M., Nagarajan, V., Li, Z., Rawat, A. S., Zaheer, M., Menon, A. K., and Kumar, S · 2023
Cited alongside, same era.
Counterfactual memorization in neural language models
Zhang, C., Ippolito, D., Lee, K., Jagielski, M., Tramèr, F., and Carlini, N · 2023
Cited alongside, same era.
Large language models can learn rules
Zhu, Z., Xue, Y., Chen, X., Zhou, D., Tang, J., Schuurmans, D., and Dai, H · 2023
Cited alongside, same era.
Qi, Z., Luo, H., Huang, X., Zhao, Z., Jiang, Y., Fan, X., Lakkaraju, H., and Glass, J · 2024
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Vision language models are blind
Rahmanzadehgervi, P., Bolton, L., Taesiri, M. R., and Nguyen, A. T · 2024
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Rewarding progress: Scaling automated process verifiers for LLM reasoning
Setlur, A., Nagpal, C., Fisch, A., Geng, X., Eisenstein, J., Agarwal, R., Agarwal, A., Berant, J., and Kumar, A · 2024
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Scaling LLM test-time compute optimally can be more effective than scaling model parameters
Snell, C., Lee, J., Xu, K., and Kumar, A · 2024
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Aligning large multimodal models with factually augmented RLHF
Sun, Z., Shen, S., Cao, S., Liu, H., Li, C., Shen, Y., Gan, C., Gui, L., Wang, Y.-X., Yang, Y., Keutzer, K., and Darrell, T · 2024
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Toward self-improvement of LLMs via imagination, searching, and criticizing
Tian, Y., Peng, B., Song, L., Jin, L., Yu, D., Mi, H., and Yu, D · 2024
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Generalization vs memorization: Tracing language models’ capabilities back to pretraining data
Wang, X., Antoniades, A., Elazar, Y., Amayuelas, A., Albalak, A., Zhang, K., and Wang, W. Y · 2024
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Tree of thoughts: Deliberate problem solving with large language models
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T., Cao, Y., and Narasimhan, K · 2024
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Physics of language models: Part 2.1, grade-school math and the hidden reasoning process
Ye, T., Xu, Z., Li, Y., and Allen-Zhu, Z · 2024
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Law of the weakest link: Cross capabilities of large language models
Zhong, M., Zhang, A., Wang, X., Hou, R., Xiong, W., Zhu, C., Chen, Z., Tan, L., Bi, C., Lewis, M., et al · 2024
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DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning, 2025
DeepSeekAI et al · 2025
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Brave: Broadening the visual encoding of vision-language models
Kar, O. F., Tonioni, A., Poklukar, P., Kulshrestha, A., Zamir, A., and Tombari, F · 2025
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