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Training safe LLMs remains a critical challenge.
Analysis of representations for domain adaptation
Ben-David, S., Blitzer, J., Crammer, K., and Pereira, F · 2006
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Concrete problems in ai safety
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., and Mané, D · 2016
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Learning to summarize with human feedback
Stiennon, N., Ouyang, L., Wu, J., Ziegler, D., Lowe, R., Voss, C., Radford, A., Amodei, D., and Christiano, P. F · 2020
Earlier work this paper cites.
A general language assistant as a laboratory for alignment
Askell, A., Bai, Y., Chen, A., Drain, D., Ganguli, D., Henighan, T., Jones, A., Joseph, N., Mann, B., DasSarma, N., et al · 2021
Earlier work this paper cites.
Program synthesis with large language models
Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C., Terry, M., Le, Q., et al · 2021
Earlier work this paper cites.
Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., de Oliveira Pinto, H. P., 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., Ryder, N., Pavlov, M., Power, A., Kaiser, L., Bavarian, M., Winter, C., Tillet, P., Such, F. P., Cummings, D., Plappert, M., Chantzis, F., Barnes, E., Herbert-Voss, A., Guss, W. H., Nichol, A., Paino, A., Tezak, N., Tang, J., Babuschkin, I., Balaji, S., Jain, S., Saunders, W., Hesse, C., Carr, A. N., Leike, J., Achiam, J., Misra, V., Morikawa, E., Radford, A., Knight, M., Brundage, M., Murati, M., Mayer, K., Welinder, P., McGrew, B., Amodei, D., McCandlish, S., Sutskever, I., and Zaremba, W · 2021
Earlier work this paper cites.
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., Hesse, C., and Schulman, J · 2021
Earlier work this paper cites.
Measuring mathematical problem solving with the math dataset
Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., and Steinhardt, J · 2021
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Lora: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2021
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Constitutional ai: Harmlessness from ai feedback
Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., et al · 2022
Earlier work this paper cites.
Underspecification presents challenges for credibility in modern machine learning
D’Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., et al · 2022
Earlier work this paper cites.
On feature learning in the presence of spurious correlations
Izmailov, P., Kirichenko, P., Gruver, N., and Wilson, A. G · 2022
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Fine-tuning can distort pretrained features and underperform out-of-distribution
Kumar, A., Raghunathan, A., Jones, R., Ma, T., and Liang, P · 2022
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Diversify and disambiguate: Learning from underspecified data
Lee, Y., Yao, H., and Finn, C · 2022
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Introducing ChatGPT, 2022
OpenAI · 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
Earlier work this paper cites.
Solving math word problems with process-and outcome-based feedback
Uesato, J., Kushman, N., Kumar, R., Song, F., Siegel, N., Wang, L., Creswell, A., Irving, G., and Higgins, I · 2022
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Do-anything-now, 2022
Walkerspider · 2022
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Star: Bootstrapping reasoning with reasoning
Zelikman, E., Wu, Y., Mu, J., and Goodman, N · 2022
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Rich feature construction for the optimization-generalization dilemma
Zhang, J., Lopez-Paz, D., and Bottou, L · 2022
Earlier work this paper cites.
Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
Earlier work this paper cites.
Bai, J., Bai, S., Chu, Y., Cui, Z., Dang, K., Deng, X., Fan, Y., Ge, W., Han, Y., Huang, F., et al · 2023
Earlier work this paper cites.
Jailbreaking black box large language models in twenty queries
Chao, P., Robey, A., Dobriban, E., Hassani, H., Pappas, G. J., and Wong, E · 2023
Earlier work this paper cites.
Understanding and improving feature learning for out-of-distribution generalization
Chen, Y., Huang, W., Zhou, K., Bian, Y., Han, B., and Cheng, J · 2023
Earlier work this paper cites.
Ultrafeedback: Boosting language models with high-quality feedback, 2023
Cui, G., Yuan, L., Ding, N., Yao, G., Zhu, W., Ni, Y., Xie, G., Liu, Z., and Sun, M · 2023
Cited alongside, same era.
Enhancing chat language models by scaling high-quality instructional conversations
Ding, N., Chen, Y., Xu, B., Qin, Y., Zheng, Z., Hu, S., Liu, Z., Sun, M., and Zhou, B · 2023
Cited alongside, same era.
Scaling laws for reward model overoptimization
Gao, L., Schulman, J., and Hilton, J · 2023
Cited alongside, same era.
Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. d. l., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al · 2023
Cited alongside, same era.
Efficient memory management for large language model serving with pagedattention
Kwon, W., Li, Z., Zhuang, S., Sheng, Y., Zheng, L., Yu, C. H., Gonzalez, J. E., Zhang, H., and Stoica, I · 2023
Cited alongside, same era.
Harmbench: A standardized evaluation framework for automated red teaming and robust refusal
Mazeika, M., Phan, L., Yin, X., Zou, A., Wang, Z., Mu, N., Sakhaee, E., Li, N., Basart, S., Li, B., et al · 2024
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Orca-math: Unlocking the potential of slms in grade school math
Mitra, A., Khanpour, H., Rosset, C., and Awadallah, A · 2024
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Rule based rewards for language model safety
Mu, T., Helyar, A., Heidecke, J., Achiam, J., Vallone, A., Kivlichan, I., Lin, M., Beutel, A., Schulman, J., and Weng, L · 2024
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Learning to reason with llms, 2024
OpenAI · 2024
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Safety alignment should be made more than just a few tokens deep
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Lightman, H., Kosaraju, V., Burda, Y., Edwards, H., Baker, B., Lee, T., Leike, J., Schulman, J., Sutskever, I., and Cobbe, K · 2023
Cited alongside, same era.
Self-refine: Iterative refinement with self-feedback
Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al · 2023
Cited alongside, same era.
Xstest: A test suite for identifying exaggerated safety behaviours in large language models
Röttger, P., Kirk, H. R., Vidgen, B., Attanasio, G., Bianchi, F., and Hovy, D · 2023
Cited alongside, same era.
Do-not-answer: A dataset for evaluating safeguards in llms
Wang, Y., Li, H., Han, X., Nakov, P., and Baldwin, T · 2023
Cited alongside, same era.
Jailbroken: How does LLM safety training fail?
Wei, A., Haghtalab, N., and Steinhardt, J · 2023
Cited alongside, same era.
Wizardlm: Empowering large language models to follow complex instructions
Xu, C., Sun, Q., Zheng, K., Geng, X., Zhao, P., Feng, J., Tao, C., and Jiang, D · 2023
Cited alongside, same era.
Metamath: Bootstrap your own mathematical questions for large language models
Yu, L., Jiang, W., Shi, H., Yu, J., Liu, Z., Zhang, Y., Kwok, J. T., Li, Z., Weller, A., and Liu, W · 2023
Cited alongside, same era.
Qi, X., Panda, A., Lyu, K., Ma, X., Roy, S., Beirami, A., Mittal, P., and Henderson, P · 2024
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Recursive introspection: Teaching language model agents how to self-improve
Qu, Y., Zhang, T., Garg, N., and Kumar, A · 2024
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website template2, 2024
Schmid, P · 2024
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” do anything now”: Characterizing and evaluating in-the-wild jailbreak prompts on large language models
Shen, X., Chen, Z., Backes, M., Shen, Y., and Zhang, Y · 2024
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Latent adversarial training improves robustness to persistent harmful behaviors in llms
Sheshadri, A., Ewart, A., Guo, P., Lynch, A., Wu, C., Hebbar, V., Sleight, H., Stickland, A. C., Perez, E., Hadfield-Menell, D., et al · 2024
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Gemma 2: Improving open language models at a practical size
Team, G., Riviere, M., Pathak, S., Sessa, P. G., Hardin, C., Bhupatiraju, S., Hussenot, L., Mesnard, T., Shahriari, B., Ramé, A., et al · 2024
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Qwen2.5: A party of foundation models, September 2024
Team, Q · 2024
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Step-on-feet tuning: Scaling self-alignment of llms via bootstrapping
Wang, H., Ma, G., Meng, Z., Qin, Z., Shen, L., Zhang, Z., Wu, B., Liu, L., Bian, Y., Xu, T., et al · 2024
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Reward hacking in reinforcement learning
Weng, L · 2024
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Berkeley function calling leaderboard
Yan, F., Mao, H., Ji, C. C.-J., Zhang, T., Patil, S. G., Stoica, I., and Gonzalez, J. E · 2024
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Zeng, Y., Lin, H., Zhang, J., Yang, D., Jia, R., and Shi, W · 2024
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Llamafactory: Unified efficient fine-tuning of 100+ language models
Zheng, Y., Zhang, R., Zhang, J., Ye, Y., Luo, Z., Feng, Z., and Ma, Y · 2024
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Improving alignment and robustness with short circuiting
Zou, A., Phan, L., Wang, J., Duenas, D., Lin, M., Andriushchenko, M., Wang, R., Kolter, Z., Fredrikson, M., and Hendrycks, D · 2024
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rstar-math: Small llms can master math reasoning with self-evolved deep thinking
Guan, X., Zhang, L. L., Liu, Y., Shang, N., Sun, Y., Zhu, Y., Yang, F., and Yang, M · 2025
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., Zhu, Q., Ma, S., Wang, P., Bi, X., et al · 2025
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Deepscaler: Surpassing o1-preview with a 1.5b model by scaling rl, 2025
Luo, M., Tan, S., Wong, J., Shi, X., Tang, W. Y., Roongta, M., Cai, C., Luo, J., Li, L. E., Popa, R. A., and Stoica, I · 2025
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Scaling laws of synthetic data for language models
Qin, Z., Dong, Q., Zhang, X., Dong, L., Huang, X., Yang, Z., Khademi, M., Zhang, D., Awadalla, H. H., Fung, Y. R., et al · 2025
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Kimi k1. 5: Scaling reinforcement learning with llms
Team, K., Du, A., Gao, B., Xing, B., Jiang, C., Chen, C., Li, C., Xiao, C., Du, C., Liao, C., et al · 2025
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Lifelong safety alignment for language models, 2025
Wang, H., Qin, Z., Zhao, Y., Du, C., Lin, M., Wang, X., and Pang, T · 2025
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