Why inter-domain reasoning is important in large-language models (LLMS)
Recent breakthroughs in LRMs, in particular those formed using long COT techniques, show that they can generalize impressively in different fields. Interestingly, the models formed on tasks such as mathematics or coding often work well in unrelated areas, such as logical puzzles or creative writing. However, which allows this flexibility is not entirely clear. One possible explanation is that these models learn the basic reasoning models, called the prototypes of abstract reasoning, which cut the domains. These shared cognitive structures allow the model to focus less on how problems are presented and more on the similar reflection processes necessary to solve them, allowing a broader transfer.
From Cot to RL: a change in the way LLM learn to reason
Recent progress in Great language model The reasoning has gone from a simple bed of bed and a supervised fine adjustment to RL. Models like Deepseek-R1 and the Sewn Graines-V1.5 have improved long COT reasoning thanks to mathematical problems, logical tasks and code execution. These models use RL techniques guided by verifiable rewards, such as the accuracy of responses to the ground, to explore complex reasoning paths. This approach allows models to learn errors, break down complex problems and refine solutions by iteration. Unlike past methods, this work presents the concept of “reasoning prototypes” to better understand the fundamental thinking models that allow models to generalize in very different fields.
Protoreason frame: Structured reasoning with Prolog and PDDL
Researchers from Bytedance Seed and Shanghai Jiao Tong University have developed Protoreasoning, a frame designed to improve reasoning in large -language models using structured prototype representations, such as Prolog and PDDL. This system includes an automated pipeline to translate problems in these formats, a reliable verification configuration using performers and a synthesis of evolutionary problems without manual labeling. The models formed on these prototypes have shown notable improvements between various tasks, including logical reasoning (+ 4.7%), planning (+ 6.3%), general reasoning (+ 4.0%) and mathematics (+ 1.0%). Above all, training in this structured “prototype space” has led to better generalization through similar tasks, supporting the idea that abstract reasoning models improve the performance of the cross -domain.
Architecture presentation: manufacturer's system and prototype verifier
The Protor-Lerisonnant framework stimulates reasoning in LLM using structured prototypes, PROGROG for logic and PDD for planning. It includes two basic modules: a prototype manufacturer that translates natural language problems into formal representations and a verification system which checks the accuracy of the solution. For Prolog, a four-step pipeline generates various logical problems, which are checked using SWI-PRolog. For planning, tasks such as plan generation, completion and reorganization are built using PDDL, with correction verified via the Val validator. The training process includes the distillation of the teachers' model for reasoning paths, sampling based on difficulties and filtering to ensure that high quality data refine the robust generalization model.
Evaluations show measurable improvements in reasoning and planning
The proto-seasoning frame was evaluated by experiments using a 150B parameter mixture model (15B active), formed on an organized set of samples of Prolog and PDD organized. The results have shown consistent improvements through logical reasoning, planning and general references, in particular MMLU and likes 2025. A key ablation study compared PROLG -based training with NL versions on corresponding data sets. The two formats significantly outperformed the basic line, Prolog performing performance almost equal to NL. This shows that the structured prototype formation can be applied to natural language tasks. However, the explicit reasoning (for example, chain of thoughts) is crucial and the low -sample categories have shown lower gains due to insufficient data.
Key results and theoretical implications of reasoning prototypes
In conclusion, Protoreasoning, a framework is based on the idea that the prototypes of abstract reasoning like PROLOG for logic and the PDD for planning allow models of large languages to generalize between the fields. By forming models on these structured representations, the study observed notable improvements in logical reasoning, planning and general tasks of problem solving. The results support the hypothesis that the shared reasoning models through the fields facilitate knowledge transfer in models. Although the empirical results are promising, the exact nature of the reasoning prototypes remains theoretically under-explored. Future work will aim to formalize these mathematically concepts and to validate the results by using models and sets of open source data.
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Sana Hassan, consulting trainee at Marktechpost and double -degree student at Iit Madras, is passionate about the application of technology and AI to meet the challenges of the real world. With a great interest in solving practical problems, it brings a new perspective to the intersection of AI and real life solutions.
