Pith Number

pith:MVZX2MDL

pith:2026:MVZX2MDLLMFELBEO5K6KJ6JUEQ

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Learning Decentralized LLM Collaboration with Multi-Agent Actor Critic

Christopher Amato, Ryan Amiri, Shuo Liu, Tianle Chen

Centralized critic in multi-agent actor-critic training outperforms decentralized critics and Monte Carlo methods for LLM collaboration on long-horizon or sparse-reward tasks.

arxiv:2601.21972 v5 · 2026-01-29 · cs.AI · cs.DC · cs.MA

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\usepackage{pith}
\pithnumber{MVZX2MDLLMFELBEO5K6KJ6JUEQ}

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3 Author claim open · sign in to claim

4 Citations open

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Claims

C1strongest claim

Our experiments across writing, coding, and game-playing domains show that Monte Carlo methods and CoLLM-DC can achieve performance comparable to CoLLM-CC in short-horizon and dense-reward settings. However, they both underperform CoLLM-CC on long-horizon or sparse-reward tasks, where Monte Carlo methods require substantially more samples and CoLLM-DC struggles to converge.

C2weakest assumption

That LLM collaboration tasks can be reliably cast as multi-agent reinforcement learning problems with reward functions that accurately capture collaboration quality and that the environments admit stable actor-critic training.

C3one line summary

Multi-agent actor-critic methods with a centralized critic improve decentralized LLM collaboration over Monte Carlo baselines in long-horizon and sparse-reward settings.

Cited by

5 papers in Pith

Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems

Improving the Efficiency of Language Agent Teams with Adaptive Task Graphs

Cloud-native and Distributed Systems for Efficient and Scalable Large Language Models -- A Research Agenda

Reinforcement Learning for LLM-based Multi-Agent Systems through Orchestration Traces

Receipt and verification

First computed	2026-05-27T02:05:11.145749Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

65737d306b5b0a45848eeabca4f934242b330a2c5975ce8c2b98b6c97682bc42

Aliases

arxiv: 2601.21972 · arxiv_version: 2601.21972v5 · doi: 10.48550/arxiv.2601.21972 · pith_short_12: MVZX2MDLLMFE · pith_short_16: MVZX2MDLLMFELBEO · pith_short_8: MVZX2MDL

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/MVZX2MDLLMFELBEO5K6KJ6JUEQ \
  | 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: 65737d306b5b0a45848eeabca4f934242b330a2c5975ce8c2b98b6c97682bc42

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "86e36f80e49118bb992192790a62311a5c4065d8f108d1649872b50191e1023b",
    "cross_cats_sorted": [
      "cs.DC",
      "cs.MA"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-01-29T16:50:30Z",
    "title_canon_sha256": "0bb197f8c5b911f34c691736b338cd4768da8ffca98275306943a7a6e6dc6482"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2601.21972",
    "kind": "arxiv",
    "version": 5
  }
}