Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models
Summary
Paper digest
What problem does the paper attempt to solve? Is this a new problem?
The paper aims to address two key challenges in the context of large language models (LLMs) and symbolic reasoning: the scarcity of symbolic training data and the limited proficiency of LLMs in symbolic reasoning . This problem is not entirely new, as previous approaches have attempted to enhance LLMs in symbolic reasoning tasks, but they have limitations such as the need for strong LLMs as teachers or reliance on reinforcement learning algorithms with human annotations for reward model training . The proposed approach, called Environment-guided (Env-guided) self-training, introduces a novel framework named ENVISIONS that iteratively trains LLMs through interactions with an embodied environment, aiming to overcome the challenges of data scarcity and LLM proficiency in symbolic reasoning .
What scientific hypothesis does this paper seek to validate?
This paper aims to validate the hypothesis that the proposed environment-guided neural-symbolic self-training framework, ENVISIONS, can effectively address two main challenges in the context of Large Language Models (LLMs):
- Overcoming the scarcity of symbolic data.
- Enhancing the limited proficiency of LLMs in processing symbolic language .
What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?
The paper proposes a novel neural-symbolic self-training framework called ENVISIONS for large language models (LLMs) . This framework aims to enhance the proficiency of LLMs in processing symbolic language (SL) without the need for human-annotated data . ENVISIONS involves iterative training of LLMs through interactions with an embodied environment, enabling the models to generate trajectories and learn symbolic language processing abilities . The key idea is to empower LLMs to self-correct and improve their performance through self-refining loss and self-rewarding algorithms .
The paper introduces the concept of Environment-guided (Env-guided) self-training, which is a new approach to training LLMs . This method involves training LLMs through interactions with an embodied environment, generating correct-incorrect trajectory pairs to enhance the models' capabilities . By leveraging the interactive nature of the environment, LLMs can learn symbolic language processing abilities and improve their performance without relying on human annotations .
ENVISIONS is designed to eliminate the need for human annotation or a stronger teacher model during self-training . It focuses on converting existing LLMs from weak to strong in neural-symbolic scenarios by addressing the challenges of limited symbolic training data and the weaknesses of LLMs in SL tasks . The framework enables LLMs to continuously explore correct trajectories, leading to improved performance, especially in logic reasoning tasks .
The paper also discusses the limitations of previous self-training approaches, such as Distill-then-Finetune and Reinforced Self-Training methods, and highlights the inefficiencies and constraints associated with these methods . ENVISIONS aims to overcome these limitations by providing a more efficient and sustainable option for training LLMs in neural-symbolic scenarios .
Overall, ENVISIONS offers a promising approach to enhancing LLMs' capabilities in processing symbolic language by leveraging self-training through interactions with an embodied environment, thereby improving performance and reducing reliance on external models or human annotations . The ENVISIONS framework introduces several key characteristics and advantages compared to previous methods outlined in the paper :
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Neural-Symbolic Self-Training Framework: ENVISIONS is a novel neural-symbolic self-training framework designed to enhance Large Language Models (LLMs) in processing symbolic language without human-annotated data . This framework focuses on converting LLMs from weak to strong in neural-symbolic scenarios by addressing the challenges of limited symbolic training data and the weaknesses of LLMs in symbolic language tasks .
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Environment-Guided Self-Training: ENVISIONS utilizes an environment-guided self-training approach, involving iterative training of LLMs through interactions with an embodied environment to generate correct-incorrect trajectory pairs for improved performance . This method enables LLMs to learn symbolic language processing abilities and enhance their performance without relying on human annotations .
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Efficiency and Sustainability: ENVISIONS demonstrates high evolutionary efficiency and sustainability, showcasing swift adaptability to different scenarios and continuous evolutionary progress compared to baseline methods . It stands out as a more sustainable option, achieving exceptional performance with minimal time for data collection .
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Superior Performance: ENVISIONS consistently outperforms strong baselines, presenting significant enhancements in average performances across different variants of LLMs . It offers greater scalability and efficiency compared to other methods, including Distill-then-Finetune, Reinforced Self-Training, and other Env-guided Self-Training approaches .
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Exploratory Ability and Stability: ENVISIONS maintains a balance between exploratory ability and stability, effectively retaining high-quality solutions during training and mitigating the issue of forgetting previous trajectories . The framework's RL-free loss enables flexible updates of LLMs, enhancing their exploration capabilities .
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Diverse Trajectories: ENVISIONS excels in synthesizing diverse trajectories, surpassing Reinforced Self-Training approaches in generating correct and unique trajectories, which is crucial for self-training and performance improvement . The framework's ability to maintain diversity in trajectories contributes to its superiority over other methods .
In conclusion, ENVISIONS offers a comprehensive and innovative approach to self-training LLMs in neural-symbolic scenarios, providing significant advantages in performance, efficiency, sustainability, and trajectory diversity compared to existing methods .
Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?
Several related research studies have been conducted in the field of neural-symbolic self-training frameworks for large language models. Noteworthy researchers in this area include Fangzhi Xu, Qiushi Sun, Kanzhi Cheng, Jun Liu, Yu Qiao, and Zhiyong Wu . The key to the solution proposed in the paper is the development of an environment-guided neural-symbolic self-training framework named ENVISIONS. This framework aims to address the challenges of scarcity of symbolic data and the limited proficiency of Large Language Models (LLMs) in processing symbolic language. ENVISIONS has been shown to be effective in overcoming these challenges through extensive evaluations across three distinct domains .
How were the experiments in the paper designed?
The experiments in the paper were designed to primarily cover three domains: web agent, math reasoning, and logic reasoning. The experiments involved test tasks in these domains, such as MiniWob++ for the web agent domain, GSM8K, MATH, GSM-Hard, SVAMP, and AsDiv for math reasoning, and ProofWriter and RuleTaker for logic reasoning. These tasks were evaluated under the zero-shot setting, with specific details provided for each task in terms of the number of test samples, beam size, and maximum length . The experiments aimed to assess the performance and capabilities of the proposed self-training framework ENVISIONS across these diverse domains and tasks, providing a comprehensive evaluation of the framework's effectiveness and applicability .
What is the dataset used for quantitative evaluation? Is the code open source?
The dataset used for quantitative evaluation in the study is the MiniWob++ benchmark, which includes various tasks for evaluation . The code for the framework is open source, as indicated by the URL provided in the document: https://github.com/deepseek-ai/DeepSeek-LLM .
Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.
The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The paper extensively evaluates the proposed neural-symbolic self-training framework ENVISIONS across various domains, including agent tasks, math reasoning, and logical reasoning . The results demonstrate significant improvements in performance metrics such as agent performance, math reasoning, and logic reasoning when compared to other self-training approaches like Reinforced Self-Training and Iterative SFT+DPO . This indicates that ENVISIONS outperforms existing methods in enhancing the proficiency of Large Language Models (LLMs) in processing supervised learning tasks .
Moreover, the paper conducts in-depth analyses from multiple perspectives, such as exploring the evolution progress of self-training methods, the scaling of hyperparameters, and the comparison of different training strategies . These analyses provide a comprehensive understanding of the framework's effectiveness and efficiency in addressing the challenges faced by previous approaches in empowering LLMs in neural-symbolic scenarios .
Overall, the experiments and results in the paper offer robust empirical evidence supporting the effectiveness of the proposed ENVISIONS framework in achieving the research objectives of enhancing LLMs' capabilities in processing symbolic tasks without the need for human-annotated data . The detailed performance evaluations, comparisons with baseline methods, and insightful analyses validate the scientific hypotheses put forth in the study and highlight the superiority of ENVISIONS in training LLMs for various tasks.
What are the contributions of this paper?
The paper makes several contributions in the field of large language models and self-training frameworks:
- It introduces the ENVISIONS framework, which combines high evolutionary efficiency and sustainability, demonstrating swift adaptability to different scenarios with exceptional performance achieved in minimal time for data collection .
- The paper highlights the limitations of reinforced baselines and self-rewarding strategies during iterations, showing that the incorporation of reinforced loss restricts the evolutionary scales of large language models, while self-rewarding exhibits reduced benefits over time .
- ENVISIONS stands out as a more sustainable option compared to other baselines, showcasing continued evolutionary progress even after other methods reach saturated performance levels .
What work can be continued in depth?
Further research in this area can delve deeper into the following aspects:
- Exploration Efficiency: Investigating the efficiency of exploration in neural-symbolic scenarios, particularly focusing on how policy LLMs rapidly explore correct samples and mitigate the issue of forgetting previously-solved samples .
- Impact of Key Components: Conducting in-depth analysis on the impact of key components in frameworks like ENVISIONS, such as self-refinement-oriented optimizations and the design of L2 loss, to understand their effectiveness in boosting performances .
- Training Strategies: Exploring different training strategies, like continuous training based on previous checkpoints versus optimizing the policy LLM from scratch in each iteration, to determine their impact on stability and performance in self-training frameworks .
- Generalization to Various Backbones: Assessing the generalizability of self-training frameworks like ENVISIONS across different base Large Language Models (LLMs) to enhance their capabilities in various tasks, such as mathematical reasoning .
- Sustainability and Efficiency: Evaluating the sustainability and efficiency of self-training methods compared to reinforced self-training approaches, highlighting the advantages of frameworks like ENVISIONS in synthesizing diverse trajectories and achieving superior performance .
- Enhancing Proficiency in Symbolic Language: Addressing the limited proficiency of LLMs in processing symbolic language by developing innovative approaches that empower LLMs to interact with embodied environments for iterative training .
- Data Scarcity Mitigation: Further exploring methods to mitigate the scarcity of symbolic training data by enabling policy LLMs to autonomously interact with environments to produce candidate symbolic solutions through online exploration .