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The gpt-oss models from OpenAI are a synthesis of ideas from prior research. Here are 10 interesting papers that were directly used in gpt-oss…
(1) Longformer: Introduces sliding window attention, a form of sparse attention that is utilized in alternating layers of both gpt-oss models.
(2) StreamingLLM: Describes the concept of attention sinks in large language models (LLMs)—these are tokens within a sequence that the model assigns high attention or weight to, simply because the softmax operation prevents the model from assigning attention to no tokens at all.
(3) Off-by-one attention: Proposes a solution to attention sinks by allowing the attention mechanism to assign no attention to any token. This is achieved by adding a bias term of 1 to the denominator of the softmax operation within attention. In gpt-oss models, a similar approach is used, but the bias term is learned rather than fixed at 1.
(4) Switch Transformer: Presents several ideas foundational to modern mixture-of-experts (MoE) based LLMs. It’s important to note that many other papers, in addition to Switch Transformer, have contributed to this field.
(5) RMSNorm: A streamlined variant of layer normalization that is both more efficient and has fewer trainable parameters. Both gpt-oss models employ RMSNorm.
(6) RoPE: Stands for Rotary Positional Encoding, a hybrid absolute/relative positional encoding method used by gpt-oss models. RoPE encodes absolute position using a rotation matrix and incorporates relative position information directly into the self-attention mechanism.
(7) YaRN: A method for extending the context window in LLMs, which is adopted by gpt-oss models. YaRN works by adjusting the frequency basis used within RoPE and further training the LLM to handle longer contexts.
(8) Flash Attention: Utilized by gpt-oss models, flash attention leverages system-level optimizations to significantly improve the computational and memory efficiency of the attention operation.
(9) DeepSeek-R1: While the specific reasoning or reinforcement learning (RL) training strategies used by gpt-oss models are not fully detailed, the DeepSeek-R1 technical report offers a comprehensive overview of how RL training with verifiable rewards is implemented at scale.
(10) Deliberative alignment: This is the safety training approach used by gpt-oss models, designed to teach the models how to reason through safety specifications and determine when it is appropriate to refuse a request.### Cameron R. Wolfe, Ph.D. Author
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Research @ Netflix
](https://www.linkedin.com/in/cameron-r-wolfe-ph-d-04744a238)
Here are all of the referenced papers / resources:
[1] Beltagy, Iz, Matthew E. Peters, and Arman Cohan. “Longformer: The long-document transformer.”
[2] Xiao, Guangxuan, et al. “Efficient streaming language models with attention sinks.”
[3] Miller, Evan, et al. “Attention Is Off By One”
[4] Fedus, William, Barret Zoph, and Noam Shazeer. “Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.”
[5] Zhang, Biao, and Rico Sennrich. “Root mean square layer normalization.”
[6] Su, Jianlin, et al. “Roformer: Enhanced transformer with rotary position embedding.”
[7] Peng, Bowen, et al. “Yarn: Efficient context window extension of large language models.”
[8] Dao, Tri, et al. “Flashattention: Fast and memory-efficient exact attention with io-awareness.”
[9] Guo, Daya, et al. “Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning.”
[10] Guan, Melody Y., et al. “Deliberative alignment: Reasoning enables safer language models.”### Ashish Kumar • 3rd+
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🧠 Building Scalable AI/ML, MLOps & Data Engineering Platforms | Technical Lead @ Thales | Expert in DevOps & Cloud Platforms | Python, ETL, Databricks | GCP, AWS, Azure | K8s, Terraform | MLflow, Vertex AI, SageMaker.
](https://www.linkedin.com/in/ashish-kumar-41188132)### Vaibhav Rathi • 3rd+
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Senior Data Scientist crafting innovative AI & Gen AI Solutions at Fractal
](https://www.linkedin.com/in/vaibhav-rathi-301b7797)### Kiran Dugana • 3rd+
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Developing AI medha for India with CoE at National Informatics centre
](https://www.linkedin.com/in/kiran-dugana-87b962116)### Shivam Bharadwaj • 2nd
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AI Engineering@Forus | Cognitive Neuroscience
](https://www.linkedin.com/in/shivam-bharadwaj-ai)
(edited)
What I like about this post is how clearly it shows that gpt oss models are the product of layered innovation, drawing from multiple landmark research papers. Each of these contributions solves a specific challenge in LLM design. From handling long context with Longformer and StreamingLLM, to boosting attention efficiency with Flash Attention and Off by one attention, to architectural advances like Switch Transformer and smarter normalization via RMSNorm, every element plays a role. Together, they create models that are more efficient, scalable, and capable. This shows that modern AI is built on cumulative progress rather than a single breakthrough.### Sairam Sundaresan • 2nd
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AI Engineering Leader | I help engineers land AI roles and companies build valuable products
](https://www.linkedin.com/in/sairam-sundaresan)### Honggang Zhang (张宏纲) • 2nd
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Professor, Zhejiang University; IEEE Fellow, AAIA Fellow, AIIA Fellow; Founding Chief Managing Editor, Intelligent Computing, a Science Partner Journal (SPJ)