"CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidance"

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https://arxiv.org/abs/2502.04350

The paper addresses the challenge of effectively steering LLMs to utilize symbolic computing through code generation, alongside textual reasoning, for complex tasks. Current methods often fail to optimally decide when to use code versus text, underutilizing the power of symbolic computation.

This paper introduces CodeSteer, a framework that guides LLMs in code/text generation through multi-round interactions, enhancing their ability to solve symbolic tasks by leveraging code when appropriate.

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📌 CodeSteer introduces a practical hierarchical approach. A smaller, fine-tuned model effectively guides a larger LLM in complex symbolic tasks. This demonstrates efficient resource utilization.

📌 Multi-round guidance with dynamic adaptation is a key innovation. CodeSteer mimics iterative problem-solving. It allows for error correction and method switching during the task.

📌 Symbolic and Self-answer checkers are crucial components. They enhance CodeSteer's ability to evaluate code complexity and ensure answer correctness. This improves overall system reliability.

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Methods Explored in this Paper 🔧:

→ CodeSteer is proposed as an assistant framework to guide a TaskLLM for effective code and text generation.

→ It uses a fine-tuned Llama-3-8B model, CodeSteerLLM, to guide larger models like GPT-4o.

→ CodeSteer operates in multiple rounds, reviewing TaskLLM's answers and providing guidance for subsequent rounds.

→ It incorporates two checkers: a Symbolic Checker to assess code complexity and a Self-answer Checker for answer verification through code execution.

→ CodeSteerLLM is fine-tuned using supervised fine-tuning (SFT) and direct preference optimization (DPO) on a dataset of multi-round guidance trajectories.

→ A novel multi-round SFT approach is used, emphasizing the final two rounds of guidance to address gradient cancellation issues.

→ DPO is used to further refine CodeSteerLLM based on performance-scored guidance pairs, incentivizing efficient problem-solving.

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Key Insights 💡:

→ Symbolic computing via code generation is crucial for complex reasoning and planning tasks where textual reasoning alone falls short.

→ Effective steering of LLMs between code and text is essential to fully harness symbolic computing capabilities.

→ Multi-round guidance and dynamic adaptation are key to improving LLM performance in complex tasks by mimicking iterative "executing and exploring" processes.

→ Specialized checkers, like Symbolic and Self-answer Checkers, significantly enhance the efficiency of both dataset synthesis and the performance of the guiding model.

→ Smaller, specialized models like CodeSteerLLM can effectively guide larger LLMs to leverage symbolic computing, improving overall performance and generalizability.

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Results 📊:

→ GPT-4o augmented with CodeSteer achieves an average normalized score of 86.4 on SymBench, a benchmark of 37 symbolic tasks.

→ This outperforms GPT-4o alone (53.3) and other strong LLMs like OpenAI o1 (82.7), o1-preview (74.8), and DeepSeek R1 (76.8).

→ CodeSteer demonstrates strong generalizability, providing an average performance boost of 41.8 on Claude, Mistral, and GPT-3.5 models.