fine-tuning-large-corpus-real-api-call-traces-markedly-improvesmechanismsingle paper
Fine-tuning on a large corpus of real API-call traces markedly improves multi-tool task completion in open models.
Capability: Using the tools it is given
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- Fine-tuning on a large corpus of real API-call traces markedly improves multi-tool task completion in open models.
Disagreeing is the most useful thing you can do here. Both sides of every contested claim in this catalog were assembled by the same person, which is its weakest point.
Related claims
- For long-horizon tool-calling agents (tasks needing five or more calls), embedding state-transition cues — preconditions, invariants, completion states — into the descriptions of the tools on the intended chain is what actually steers the agent's trajectory; runtime corrective text appended to tool results only patches residual drift, and the plausible user prompt alone (persona, deadlines, format constraints) does not establish the trajectory at all.Following instructions hidden in data · unreviewed
- A linear probe on a tool-calling model's hidden state at the last generated token detects incorrect tool-calls — including wrong-but-well-typed argument values that parsers and logs do not catch — and works better on larger models and at middle-to-late layers than at the final layer.Using the tools it is given · unreviewed
- For locally deployed open-weight models driving a stateful, dependency-ordered MCP tool server, cutting tool descriptions from full specifications (purpose, parameter semantics, constraints, failure conditions) down to one sentence each raises the fraction of calls the server rejects for every model tested, while its effect on task coverage is less consistent.Using the tools it is given · unreviewed
- When an agent must work over a very large tool catalogue, wrapping each tool in a natural-language interface that resolves the API schema internally — so raw schemas are retrieved on demand rather than enumerated in the model's prompt — contributes more to multi-step tool- call success than the surrounding planner, router, or verifier stages.Using the tools it is given · unreviewed
- Self-supervised training on tool-call examples teaches a model when and how to call simple tools, narrowing the gap on tasks that need them.Using the tools it is given