Tool use / function calling
ToolRL
ToolRL: Reward is All Tool Learning Needs
Superseded baseline#17 of 55 most-superseded · first seen Apr 16, 2025
Superseded — cited as a baseline and beaten by newer methods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.
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 beats ToolRL
72.90 vs 64.23
Overall · [Llama3.2-3B-Instruct]
R2IF: Aligning Reasoning with Decisions via Composite Rewards for Interpretable LLM Function Calling
What to use instead
Recent methods in the same sub-problem, not yet superseded in the knowledge base — arXiv benchmark leaders, not vetted production recommendations.