pith:DCDPKVRX
Tool Learning with Foundation Models
Foundation models learn tool use by decomposing tasks into subtasks, reasoning to adjust plans, and selecting the right tools for each step.
arxiv:2304.08354 v3 · 2023-04-17 · cs.CL · cs.AI · cs.LG
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\pithnumber{DCDPKVRXTDIYEJSDLF7FJSWYFP}
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Claims
We formulate a general tool learning framework: starting from understanding the user instruction, models should learn to decompose a complex task into several subtasks, dynamically adjust their plan through reasoning, and effectively conquer each sub-task by selecting appropriate tools.
That foundation models can be effectively trained and prompted to follow the proposed decomposition-reasoning-tool-selection process at scale, and that experiments with 18 tools sufficiently demonstrate this potential without detailed methodology or baselines in the provided abstract.
The paper reviews tool learning research, formulates a framework for foundation models to decompose tasks and use tools via reasoning, and demonstrates current models' capabilities through experiments with 18 tools.
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| First computed | 2026-05-17T23:38:47.770663Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
1886f5563798d1822643597e54cad82bc3209fdbb2bc6bc31a5549acfed333f8
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/DCDPKVRXTDIYEJSDLF7FJSWYFP \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 1886f5563798d1822643597e54cad82bc3209fdbb2bc6bc31a5549acfed333f8
Canonical record JSON
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