Pith Number

pith:FY7XKCPD

pith:2026:FY7XKCPDIB2RFDHSMQO75EI2PF

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Curiosity-Critic: Cumulative Prediction Error Improvement as a Tractable Intrinsic Reward for World Model Training

Haicheng Wang, Vin Bhaskara

Curiosity-Critic uses cumulative prediction error improvement as an intrinsic reward for world model training, estimated via a co-trained critic.

arxiv:2604.18701 v3 · 2026-04-20 · cs.LG · cs.AI · stat.ML

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\pithnumber{FY7XKCPDIB2RFDHSMQO75EI2PF}

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Record completeness

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3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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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.

Claims

C1strongest claim

Experiments on a stochastic grid world show that Curiosity-Critic outperforms prediction-error, visitation-count, and Random Network Distillation methods in training speed and final world model accuracy.

C2weakest assumption

The learned critic converges well before the world model saturates, providing a reliable online estimate of the asymptotic error baseline without oracle knowledge of the noise floor.

C3one line summary

Curiosity-Critic rewards the improvement in cumulative prediction error via a tractable per-step surrogate (current error minus learned asymptotic baseline), outperforming prior curiosity methods in a stochastic grid world.

Cited by

1 paper in Pith

Online Reward-Punishment Learning from Fixed-Channel Perceptual Event Streams without Environment Rewards

Receipt and verification

First computed	2026-06-19T16:09:58.264685Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

2e3f7509e34075128cf2641dfe911a7969e38e3d397a8d6b2af24b7a6bd55575

Aliases

arxiv: 2604.18701 · arxiv_version: 2604.18701v3 · doi: 10.48550/arxiv.2604.18701 · pith_short_12: FY7XKCPDIB2R · pith_short_16: FY7XKCPDIB2RFDHS · pith_short_8: FY7XKCPD

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/FY7XKCPDIB2RFDHSMQO75EI2PF \
  | 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: 2e3f7509e34075128cf2641dfe911a7969e38e3d397a8d6b2af24b7a6bd55575

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "02cb1f8bece5d1793abd1b5ac478bf11832fb7f3098d349cc55835e5e8912221",
    "cross_cats_sorted": [
      "cs.AI",
      "stat.ML"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-04-20T18:01:15Z",
    "title_canon_sha256": "6168d3d9a9a87b910e40adb8d3458536f23bc9972783af382d101ed5f2e0bb99"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.18701",
    "kind": "arxiv",
    "version": 3
  }
}