pith:7R3KALCW
Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies
A single generalist policy trained across a robot fleet reaches 95% success rate as experience accumulates.
arxiv:2605.00416 v2 · 2026-05-01 · cs.RO
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\pithnumber{7R3KALCWAIBC6ZW7UNM2A3ARTT}
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Record completeness
Claims
A single generalist policy improves as fleet experience accumulates, reaching an average success rate of 95%, with the largest gains on long-horizon tasks.
That the combination of DIVL for robust value estimation and QAM for policy extraction can stabilize learning from heterogeneous, sparse-reward data collected across a robot fleet without additional mechanisms for handling noise or bias in human interventions.
Fleet-scale RL framework improves a single generalist VLA policy from deployment data to 95% average success on eight real-world manipulation tasks with 16 dual-arm robots.
Receipt and verification
| First computed | 2026-06-04T01:08:50.710552Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
fc76a02c5602022f66dfa359a06c119cf35d3051c549d7ced1ea3d6812efa28b
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/7R3KALCWAIBC6ZW7UNM2A3ARTT \
| 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: fc76a02c5602022f66dfa359a06c119cf35d3051c549d7ced1ea3d6812efa28b
Canonical record JSON
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