Tool calls get worse with training: TACA supervises the reasoning-to-call transition and lifts six-benchmark accuracy by 4.61 points
In multi-turn tool-use reinforcement learning, reasoning accuracy can rise early and then fall below the non-agentic baseline; TACA traces this to negative aggregate credit on tool-call tokens and instead supervises the step where reasoning turns into a tool call. Against MT-GRPO it raises mean accuracy over six reasoning benchmarks by 4.61 and 3.61 percentage points for Qwen2.5-1.5B and 3B, while tool execution success during training stays at 86.28%.