GAPD: Gold-Action Policy Distillation for Agentic Reinforcement Learning in Knowledge Base Question Answering
arXiv:2605.29584v2 Announce Type: replace Abstract: Reinforcement learning (RL) is a natural fit for agentic knowledge base question answering (KBQA), where a model must issue executable actions, observe knowledge-base feedback, and eventually return an answer. However, current RL-based KBQA systems mainly optimize sparse rewards from the final answer, leaving intermediate action errors weakly supervised. This is especially limiting for logical-form annotated KBQA benchmarks: gold logical forms can be converted into executable action sequences, but existing pipelines use them mainly for warm-s
This development emerges as the field of AI agents increasingly focuses on robust, autonomous decision-making and interaction with complex knowledge systems, pushing beyond narrow task automation.
A strategic reader should care because improving agentic reinforcement learning in knowledge base systems directly accelerates the capability of AI agents to perform complex, multi-step Reasoning and problem-solving, impacting multiple industries.
The explicit use of gold logical forms to supervise intermediate actions in agentic KBQA systems marks a refinement in AI training methodologies, potentially leading to more reliable and interpretable agent behavior.
- · AI Agent Developers
- · Enterprise Software
- · Knowledge Base Providers
- · Researchers in Reinforcement Learning
- · Tasks requiring manual logical decomposition
- · Simplistic AI automation tools
AI agents become more proficient at complex data retrieval and reasoning tasks within defined knowledge domains.
Increased reliability of AI agents could lead to their broader adoption in critical enterprise functions requiring precision and verifiable outputs.
The enhanced capability of agentic AI to interact with and utilize structured knowledge might accelerate the 'collapse' of certain white-collar workflows not yet impacted by current AI models.
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Read at arXiv cs.CL