Inducing Reasoning Primitives from Agent Traces
📰 ArXiv cs.AI
Learn to induce reasoning primitives from agent traces to improve LLM performance and reusability
Action Steps
- Collect successful ReAct traces from agent interactions
- Cluster recurrent reasoning moves using clustering algorithms
- Convert frequent moves into pseudo-tools with natural-language docstrings
- Integrate pseudo-tools into an LLM framework for improved reasoning
- Evaluate the performance of the induced reasoning primitives on new tasks
Who Needs to Know This
AI researchers and engineers working with LLMs and agent-based systems can benefit from this technique to improve their models' reasoning capabilities
Key Insight
💡 Reasoning Primitive Induction can help unlock reusable reasoning routines from agent traces, improving LLM performance and efficiency
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🤖 Induce reasoning primitives from agent traces to supercharge your LLMs! 🚀
Key Takeaways
Learn to induce reasoning primitives from agent traces to improve LLM performance and reusability
Full Article
Title: Inducing Reasoning Primitives from Agent Traces
Abstract:
arXiv:2606.02994v1 Announce Type: new Abstract: ReAct-style LLM agents often rediscover the same reasoning routines across problems, yet leave those routines trapped in transient scratchpads. We introduce Reasoning Primitive Induction, a single-pass method that mines successful ReAct traces, clusters recurrent reasoning moves, and converts the most frequent moves into a compact library of typed pseudo-tools. Each pseudo-tool is specified by a natural-language docstring interpreted by an LLM at i
Abstract:
arXiv:2606.02994v1 Announce Type: new Abstract: ReAct-style LLM agents often rediscover the same reasoning routines across problems, yet leave those routines trapped in transient scratchpads. We introduce Reasoning Primitive Induction, a single-pass method that mines successful ReAct traces, clusters recurrent reasoning moves, and converts the most frequent moves into a compact library of typed pseudo-tools. Each pseudo-tool is specified by a natural-language docstring interpreted by an LLM at i
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