Method Drift›Tool use / function calling
ToolRL
ToolRL: Reward is All Tool Learning NeedsTool use / function calling · first seen Apr 16, 2025
superseded — cited as a baseline and beaten by newer methods
1 papers critique it · 1 beat it on benchmarks
What papers say
Verbatim critique sentences, each from a paper that cites ToolRL as a baseline.
“ToolRL~qian2025toolrl enriches the reward with tool-name and argument-quality components but applies the result as a single trajectory-level scalar, so it refines what the reward evaluates without changing which segment each signal reaches.”
— Knowing When to Ask: Segment-Level Credit Assignment for LLM Tool Use
Beaten on benchmarks
Head-to-head results where a newer method reports beating ToolRL. Values are copied from the source paper's tables — verify against the cited paper.
- R2IF: Aligning Reasoning with Decisions via Composite Rewards for Interpretable LLM Function Calling
R2IF beats ToolRL · Overall [Qwen2.5-1.5B-Instruct]
54.39 vs 54.04
- R2IF: Aligning Reasoning with Decisions via Composite Rewards for Interpretable LLM Function Calling
R2IF beats ToolRL · Overall [Qwen2.5-3B-Instruct]
57.71 vs 51.44
- R2IF: Aligning Reasoning with Decisions via Composite Rewards for Interpretable LLM Function Calling
R2IF beats ToolRL · Overall [Qwen2.5-7B-Instruct]
72.14 vs 71.57
- R2IF: Aligning Reasoning with Decisions via Composite Rewards for Interpretable LLM Function Calling
R2IF beats ToolRL · Overall [Llama3.2-3B-Instruct]
72.21 vs 66.26
Newer alternatives
Recent methods in the same sub-problem, not yet superseded in the knowledge base.