Zerosearch d’Alibaba uses learning to strengthen and simulated documents to teach LLMS recovery without real -time research

Large languages ​​are now at the heart of various applications, from coding to academic tutoring and automated assistants. However, a critical limitation persists in the way these models are designed; They are trained on static data sets that become obsolete over time. This creates a fundamental challenge because linguistic models cannot update their knowledge or validate the answers against fresh and real data. Consequently, while these models demonstrate solid performance on reasoning tasks or structured requests, their responses can always include fabricated or obsolete information, reducing their reliability in the use of the real world. To maintain credibility, in particular for applications requiring updated knowledge such as news, research or product magazines, models must interact with external and profitable external data sources.

The basic problem lies in teaching these models to effectively recover and integrate external information. Although adjusted pre-training helps develop a strong basic understanding, the ability to conduct significant dynamic research is missing. The equipment of language models with this capacity introduces practical constraints. The search engines used for the recovery of external information provide a variable document quality which introduces an inconsistency in the formation of models. In addition, the integration of strengthening learning to simulate real world search requires large -scale interactions with live APIs, by raising hundreds of thousands of calls, which becomes prohibitive. This results in a bottleneck for academic research and commercial deployment, where the scalability of costs and training are essential.

Various methods have been developed to improve research and recovery capacities of language models. Certain first techniques were based on instructions based on instructions that guided the model through processes such as the generation of subquestions or the management of research in several stages. These methods, however, were strongly based on manual adjustment and often required in -depth calculation resources to ensure coherent outings. Other approaches were based on the supervised fine setting for smaller models in order to make a more targeted recovery, with models like the carCLOTH and Retollm emerging in this space. There have also been experiences with techniques such as the search for Monte Carlo Tree to extend possible response paths during dynamically inference. Solutions based on strengthening learning as well as desired and Deeprearcher have enabled models to interact directly with real search engines, offering a training experience closer to how users behave. However, these innovations still suffer from complexity, a high demand for calculation or a financial cost due to live interaction constraints.

Researchers from Tongyi Lab in Alibaba Group introduced an innovative solution called Zerosearch. This strengthening learning framework completely removes the need for live research based on APIs. Instead, he uses another language model to simulate the behavior of a search engine. The simulation model is refined thanks to supervised training to generate documents that help or mislead the policy model, depending on whether the content is designed to be relevant or noisy. This allows complete control over the quality and cost of the document while allowing a realistic recovery training experience. A key innovation lies in the use of learning -based learning during training, which means gradually introduce more difficult recovery tasks by adjusting the amount of noise present in the generated documents. This progression helps the policy model to develop resilience and better reasoning skills over time without ever making a real request for research.

Zerosearch's structure involves distinct phases in the reasoning process. The model first thinks internally using designated tags, then generates requests if it determines that additional information is necessary. Finally, he only listens to an answer when a sufficient context is acquired. This structured approach applies the clarity of decision -making and has proven to improve the transparency and the quality of the responses. A minimum change in the prompt guides the generation of documents for the simulated search engine which controls if the document seems useful or deceptive. The simulated LLM is refined using interaction data where each recovery trajectory is labeled according to the accuracy of the final response. The policy model learns to manage simple and complex research conditions by systematically varying the quality of the document. A performance scaling function determines the quantity of noise introduced at each training stage, increasing the capacity of the model to navigate in uncertainty over time.

A 3 billion parameter model has been able to effectively simulate the recovery process for training purposes. The results have become particularly notable with larger models. A 7B recovery module was carried out at a level comparable to Google search concerning the quality of the response. A 14B model has even exceeded Google search references. Zerosearch has also shown flexibility, effectively operating in the basic LLM and instruction of different sizes. It is well integrated into a range of strengthening learning algorithms, including PPO, GRPO and strengthens ++, and it uses a reward design based on the F1 score rather than an exact correspondence to discourage the model from generating excessively long responses just to increase the overlap of keywords. In addition, Zerosearch uses a masking mechanism during retro-compaguation to ensure that the gradients are only calculated on the results of the policy model, stabilizing training without sacrificing performance.

Research demonstrates a clear and effective alternative to the confidence of the search engine in real time. The use of the generation of documents focused on simulation deletes the need for high cost API, and the quality of the drive input is accurately controlled. The method also increases the model's reasoning capacity by introducing progressive noise and uncertainty, effectively imitating the way in which the recovery of real world data could fail or mislead. The policy model is formed to extract the most useful information. These traits make Zerosearch an evolving and practical solution for commercial quality applications.

This approach identifies and successfully identifies the twin challenges of the variability of the quality of documents and the economic cost which have a limited real -time research integration in the formation of the language model. It combines the simulation of documents, structured interaction and learning to strengthen to ensure efficiency and scalability. Based only on the generation of simulated data, the researchers obtained results higher or comparable to existing methods while deleting all dependence on expensive APIs.

Several key research dishes include the following elements:

• A 3B model has effectively simulated the recovery of realistic documents with a cost of the zero API.
• A 7B recovery module corresponded to Google search performance in reference tests.
• The 14B model has exceeded the real performance of the search engine.
• Reinforcement learning was carried out with a deployment based on the curriculum which has gradually introduced noise.
• An LLM of simulation has generated relevant and noisy documents via supervised light fine adjustment.
• Structured interaction phases (,, ,, ) Improvement of the clarity and precision of the model.
• The rewards based on F1 have discouraged the hacking of reward by penalizing the unrelevant response time.
• Compatible with the main RL algorithms, including PPO, GRPO and strengthens ++.
• The training was stabilized using a gradient masking mechanism to prevent the instability of simulated tokens.

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