OpenAI releases GPT-5.6 with variable reasoning effort modes

Reasoning models have become a central feature in modern language model development following OpenAI's release of o1 approximately two years ago, which popularized the concept of LLM-based reasoning. DeepSeek-R1 followed roughly four months later and provided crucial technical details about how to train such models using a method called reinforcement learning with verifiable rewards (RLVR).

RLVR works by providing reward signals (0 for incorrect, 1 for correct) in domains where answers can be objectively verified—mathematics (using tools like SymPy or WolframAlpha) and code (using compilers, unit tests, or platforms like LeetCode). A key discovery in the DeepSeek-R1 research was that although intermediate reasoning traces were initially expected to be useful training signals, they turned out not to improve the model. Instead, providing rewards only for the final answer and response format was sufficient for the model to learn how to reason through problems, generating intermediate explanations, backtracking, and correcting its own errors—moments called "Aha" moments. The reward signal was calculated as R_total = R_accuracy + R_format, where the format reward encouraged the model to place its reasoning inside special delimiters like <think> and </think> tags.

OpenAI released the GPT-5.6 model family last week, offering three model sizes, each with roughly five or six reasoning-effort settings. This represents an evolution from the first generation of reasoning models, which were dedicated models like DeepSeek-R1 that always operated in reasoning mode and produced verbose responses regardless of task complexity. Newer approaches, such as Qwen3, introduced hybrid models capable of operating both as standard instruction-tuned models and as reasoning models on demand. Qwen3 implements this toggle through supervised fine-tuning that exposes the model to both "thinking" (reasoning) and "non-thinking" (direct answer) examples, labeled with /think and /no_think flags, and then reinforced through general reinforcement learning. At inference time, users control this behavior via a tokenizer setting like enable_thinking=True or enable_thinking=False, which effectively adds empty <think></think> tags to force the model into non-reasoning mode. GPT-5.6's reasoning-effort levels extend this concept further, allowing users to select from multiple intensity levels rather than a simple on/off switch, enabling fine-tuned trade-offs between answer quality and computational cost.

Reasoning models have become a standard feature of modern LLM releases following OpenAI's introduction of o1 nearly two years ago. DeepSeek-R1, released about four months after o1, advanced the field by publishing a detailed recipe for training such models using reinforcement learning with verifiable rewards (RLVR)—a technique that provides reward signals (correct or incorrect) for verifiable domains like mathematics and code. The key insight from DeepSeek-R1's work is that models trained this way learn to generate reasoning traces, backtrack, and self-correct without explicitly training on the intermediate reasoning steps themselves; the final-answer reward signal alone is sufficient.

OpenAI's GPT-5.6 release reflects a maturation of this approach by introducing multiple reasoning-effort levels within the same model family. Earlier dedicated reasoning models like DeepSeek-R1 were monolithic—they always generated verbose outputs with no option to disable reasoning mode. Newer models like Qwen3 demonstrated that a single model can support both reasoning and non-reasoning modes via supervised fine-tuning and reinforcement learning stages, with a toggle flag to switch between them at inference time. GPT-5.6 extends this pattern further by allowing fine-grained control over reasoning intensity, giving users and developers the ability to optimize for their specific latency and cost constraints rather than accepting a one-size-fits-all reasoning depth.

No comments yet. Be the first to share your thoughts!