Pith Number
pith:XVTQJF3D
pith:2025:XVTQJF3DPSH4ZL3DWKNN2WV3EM
not attested
not anchored
not stored
refs pending
Shaping Sparse Rewards in Reinforcement Learning: A Semi-supervised Approach
arxiv:2501.19128 v5 · 2025-01-31 · cs.LG · cs.AI
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{XVTQJF3DPSH4ZL3DWKNN2WV3EM}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
1
Bitcoin timestamp
2
Internet Archive
3
Author claim
· sign in to
claim
4
Citations
5
Replications
✓
Portable graph bundle live · download bundle · merged
state
The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same
current state with the deterministic merge algorithm.
Receipt and verification
| First computed | 2026-05-20T00:00:17.589342Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
bd670497637c8fccaf63b29add5abb2309ff4e0093405a07dd51f831c0e6a743
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/XVTQJF3DPSH4ZL3DWKNN2WV3EM \
| 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: bd670497637c8fccaf63b29add5abb2309ff4e0093405a07dd51f831c0e6a743
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "4993b5860be35b7e4fa792e808ec8a17b494af55cc02e833e8fbf4ac97e0e531",
"cross_cats_sorted": [
"cs.AI"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.LG",
"submitted_at": "2025-01-31T13:35:19Z",
"title_canon_sha256": "e960e65fa750226502b5f00ad454f5561fcfc682da4629db2c87bcf995b426c8"
},
"schema_version": "1.0",
"source": {
"id": "2501.19128",
"kind": "arxiv",
"version": 5
}
}