Pith Number

pith:Y5BR26G7

pith:2026:Y5BR26G7LCNH3OGEJYBPXFAPA6

not attested not anchored not stored refs resolved

Resolving Action Bottleneck: Agentic Reinforcement Learning Informed by Token-Level Energy

David Wipf, Henry Peng Zou, Junhua Liu, Junyou Zhu, Langzhou He, Philip S. Yu, Qitian Wu, Wei-Chieh Huang, Yue Zhou, Zhengyao Gu

Token-level signals concentrate on action tokens in agentic RL, so reweighting gradients toward them outperforms uniform policy gradients.

arxiv:2605.14558 v1 · 2026-05-14 · cs.LG · cs.AI · cs.CL

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1 Bitcoin timestamp

2 Internet Archive

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

token-level training signals, quantified by their correlations with reward variance of different rollouts sampled from a given prompt, concentrate sharply on action tokens rather than reasoning tokens, even though action tokens account for only a small fraction of the trajectory

C2weakest assumption

that down-weighting reasoning tokens and boosting high-uncertainty action tokens will not degrade the quality of the reasoning chain itself or introduce new instabilities in long-horizon trajectories

C3one line summary

ActFocus resolves the action bottleneck in agentic RL by reweighting token gradients toward action tokens using observed reward variance and an energy-based uncertainty term, outperforming PPO and GRPO by up to 65 percentage points.

References

39 extracted · 39 resolved · 17 Pith anchors

[1] Lmrl gym: Benchmarks for multi-turn reinforcement learn- ing with language models 2023

[2] Chan, Hao Sun, Samuel Holt, and Mihaela van der Schaar 2024

[3] arXiv preprint arXiv:2502.01600 , year= 2025

[4] Process reinforcement through implicit rewards,

[5] Process Reinforcement through Implicit Rewards · arXiv:2502.01456

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:05.628152Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

c7431d78df589a7db8c44e02fb940f07806e1939ab66a013470fbf4fc8a89aab

Aliases

arxiv: 2605.14558 · arxiv_version: 2605.14558v1 · doi: 10.48550/arxiv.2605.14558 · pith_short_12: Y5BR26G7LCNH · pith_short_16: Y5BR26G7LCNH3OGE · pith_short_8: Y5BR26G7

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/Y5BR26G7LCNH3OGEJYBPXFAPA6 \
  | 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: c7431d78df589a7db8c44e02fb940f07806e1939ab66a013470fbf4fc8a89aab

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "769839a6b2f431823f2ce011bc079382dda2ac288ba14a42e289655beab38918",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.CL"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-14T08:33:02Z",
    "title_canon_sha256": "381fa8f479b09790d076d448166f439f82aaf7e52afc4bf0e30ededcc7af5a81"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14558",
    "kind": "arxiv",
    "version": 1
  }
}