# Schema-Guided LLM Simulator

The work splits tool-calling RL trajectories into a tool segment and a summary segment, argues that broadcasting a single trajectory-level advantage causes cross-segment credit misattribution, and proposes SLCA-GRPO, which normalizes the two segment rewards within the group and routes each only to its own tokens inside a single unified policy, paired with a Schema-Guided LLM Simulator and Hierarchical Rewards; across Qwen2.5-3B/7B-Instruct and Qwen3-8B-Base on Toucan-Test, BFCL V3 and τ²-Bench it reports higher means than matched GRPO, with 7B gaps of +2.53, +1.36 and +9.15 percentage points.

The work splits tool-calling RL trajectories into a tool segment and a summary segment, argues that broadcasting a single trajectory-level advantage causes cross-segment credit misattribution, and proposes SLCA-GRPO, which normalizes the two segment rewards within the group and routes each only to its own tokens inside a single unified policy, paired with a Schema-Guided LLM Simulator and Hierarchical Rewards; across Qwen2.5-3B/7B-Instruct and Qwen3-8B-Base on Toucan-Test, BFCL V3 and τ²-Bench it reports higher means than matched GRPO, with 7B gaps of +2.53, +1.36 and +9.15 percentage points.