Pith Number

pith:GBMGNK6Q

pith:2026:GBMGNK6Q6ZHYSDHGILMEQ2UDMC

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StepPO: Step-Aligned Policy Optimization for Agentic Reinforcement Learning

Daoyu Wang, Enhong Chen, Jie Ouyang, Mingyue Cheng, Qi Liu, Qingchuan Li, Shuo Yu

LLM agents need step-level MDP and credit assignment rather than token-level modeling for multi-turn RL.

arxiv:2604.18401 v2 · 2026-04-20 · cs.CL

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\pithnumber{GBMGNK6Q6ZHYSDHGILMEQ2UDMC}

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3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ 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.

Claims

C1strongest claim

the conventional token-level Markov Decision Process (MDP) should be advanced to a step-level MDP formulation, and that the step, rather than the token, should be regarded as the proper action representation for LLM agents

C2weakest assumption

That redefining the MDP and credit assignment at step granularity will meaningfully address delayed/sparse rewards and long context challenges in multi-turn agent settings.

C3one line summary

StepPO argues that LLM agents should optimize at the step level rather than token level to better handle delayed rewards and long contexts in agentic RL.

Cited by

3 papers in Pith

Rethinking Agentic Reinforcement Learning In Large Language Models

Receipt and verification

First computed	2026-06-02T02:04:53.338134Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

305866abd0f64f890ce642d8486a8360a67d518fef2ed4d01d29bb17a1bed39f

Aliases

arxiv: 2604.18401 · arxiv_version: 2604.18401v2 · doi: 10.48550/arxiv.2604.18401 · pith_short_12: GBMGNK6Q6ZHY · pith_short_16: GBMGNK6Q6ZHYSDHG · pith_short_8: GBMGNK6Q

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/GBMGNK6Q6ZHYSDHGILMEQ2UDMC \
  | 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: 305866abd0f64f890ce642d8486a8360a67d518fef2ed4d01d29bb17a1bed39f

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "d92a6bb55826794aaea03e8bdd224d72c737bc0a610264b10ccbaf710c99a3c9",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2026-04-20T15:22:39Z",
    "title_canon_sha256": "9b0768938686a95fe0912f58ee7dfc52476fb36ae3bc01c12c7a51db13f1f769"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.18401",
    "kind": "arxiv",
    "version": 2
  }
}