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arxiv: 2606.02388 · v1 · pith:HPMCG4E3new · submitted 2026-06-01 · 💻 cs.LG · cs.AI

Policy and World Modeling Co-Training for Language Agents

Ning Lu , Baijiong Lin , Shengcai Liu , Jiahao Wu , Haoze Lv , Yanbin Wei , Lingting Zhu , Shengju Qian
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Xin Wang Ying-Cong Chen Qi Wang Ke Tang
This is my paper

Pith reviewed 2026-06-28 15:38 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords language agentsreinforcement learningworld modelingco-trainingauxiliary supervisionon-policy rollouts
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The pith

On-policy RL rollouts already contain the signals needed to train world models as auxiliary supervision for language agents.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper claims that reinforcement learning rollouts for language agents naturally pair each action with its resulting next observation, supplying usable supervision for a world model. It introduces PaW, which adds this world-model training as an auxiliary task during the same RL process through three components that keep the supervision stable and informative. The approach requires no separate simulators, no extra training stages, and no added computation when the agent acts later. A reader would care because it turns data already generated by standard RL into an extra learning signal that improves agent performance on benchmarks.

Core claim

PaW co-trains the policy and a world model by treating each RL transition as a supervised WM example, using action-entropy-based data selection, a noise-tolerant WM loss, and reward-adaptive loss balancing; this yields consistent gains over strong RL baselines on three agentic benchmarks across models and algorithms while leaving the inference procedure unchanged.

What carries the argument

The PaW co-training loop that adds auxiliary world-model prediction to on-policy RL rollouts via the three listed stabilization components.

If this is right

  • Standard RL rollouts become a practical source of world-model supervision without new data collection.
  • Language agents improve on task benchmarks while keeping the same inference-time computation.
  • The method works across different model sizes and RL algorithms.
  • No separate simulator or extra training stage is required.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same rollout data could be reused to train additional auxiliary predictors beyond a basic world model.
  • If the gains hold at larger scale, agent training pipelines might reduce reliance on external simulators.
  • The stabilization techniques might transfer to other auxiliary objectives that use the same transition data.

Load-bearing premise

That the three proposed components can turn the raw action-observation pairs inside ordinary RL rollouts into informative and stable world-model supervision.

What would settle it

An ablation in which removing one or more of the three components (entropy selection, noise-tolerant loss, or adaptive balancing) eliminates the reported performance gains on the agent benchmarks.

Figures

Figures reproduced from arXiv: 2606.02388 by Baijiong Lin, Haoze Lv, Jiahao Wu, Ke Tang, Lingting Zhu, Ning Lu, Qi Wang, Shengcai Liu, Shengju Qian, Xin Wang, Yanbin Wei, Ying-Cong Chen.

Figure 1
Figure 1. Figure 1: Comparison of world modeling paradigms for [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of noisy observation tokens and the effect of clipped MAE loss. (a) and (b) show two noisy WM training examples from ALFWorld and WebShop, where the same (ot, at) pair can lead to different next observation ot+1 in (a) and observations may contain random surface noise in (b). (c) shows that CE WM loss (Equation (5)) assigns a disproportionately large gradient share to noisy tokens. See Section… view at source ↗
Figure 3
Figure 3. Figure 3: Overview of PaW. PaW introduces auxiliary world modeling to agentic RL via action-entropy WM data selection, clipped MAE, and reward-adaptive loss balancing. ter an action (Zhang et al., 2025). For language agents, this becomes next-observation prediction: given (ht , at), an autoregressive model predicts the textual observation ot+1 with objective: LWM(ϕ) = −E [log πϕ(ot+1 | ht , at)] , (2) where the like… view at source ↗
Figure 5
Figure 5. Figure 5: Per-step training time and GPU memory break [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Hyperparameter sensitivity on WebShop with [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The prompt template of ALFWorld agents. Hyperparameters for Search-Augmented QA. The maximum prompt length is 4096 tokens, and the maximum response length is 512 tokens. The max turn is set to 4. The learning rate is 1e-6 for the actor. We adopt a rule-based reward, assigning a reward of 1 for success and 0 for failure. So the Rmax = 1. Invalid actions are penalized with a re￾ward of -0.01. We set the trai… view at source ↗
Figure 9
Figure 9. Figure 9: The prompt template of Search agents. provide the agent with temporal context, we addi￾tionally incorporate interaction history: we retain the two most recent history steps for ALFWorld and WebShop, and use the complete history for search-augmented question answering. The <think> </think> block is used to elicit ex￾plicit step-by-step reasoning from the agent, en￾couraging chain-of-thought (Wei et al., 202… view at source ↗
Figure 10
Figure 10. Figure 10: Policy-side training dynamics on WebShop. PaW improves the training reward over the GRPO baseline, [PITH_FULL_IMAGE:figures/full_fig_p014_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: PaW-side training dynamics. The reward-adaptive coefficient decreases as training reward improves, [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗
read the original abstract

Reinforcement learning (RL) improves large language model (LLM) agents by teaching them which actions lead to high rewards, but provides little supervision on what those actions do to the environment. World modeling (WM) can fill this gap, yet existing approaches often require separate simulators, extra training stages, or additional inference-time computation. We observe that on-policy RL rollouts already contain the needed signal: each transition pairs an action with its resulting next observation. Based on this observation, we propose PaW, a Policy and World modeling co-training framework that adds auxiliary WM supervision to the same policy during RL, without changing the inference paradigm. To make auxiliary WM supervision informative and stable, PaW introduces three components: action-entropy-based WM data selection, noise-tolerant WM loss, and reward-adaptive loss balancing. Experiments on three agentic task benchmarks show consistent improvements over strong RL baselines across models and RL algorithms. These results suggest that standard RL rollouts are a practical source of WM supervision for language-agent training.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 1 minor

Summary. The paper claims that on-policy RL rollouts already contain (action, next-observation) pairs that can supply auxiliary world-modeling supervision for LLM agents. It introduces the PaW co-training framework, which adds this supervision to the policy during RL via three stabilizing components (action-entropy-based WM data selection, noise-tolerant WM loss, and reward-adaptive loss balancing) while leaving the inference paradigm unchanged. Experiments on three agentic task benchmarks are reported to show consistent improvements over strong RL baselines across models and algorithms.

Significance. If the empirical results hold with the reported magnitude and robustness, the work would be significant because it demonstrates a practical route to WM supervision that re-uses existing RL rollouts, avoids separate simulators, extra training stages, or inference-time overhead, and thereby addresses a recognized gap in RL-based agent training.

minor comments (1)
  1. The abstract states that the three components make WM supervision 'informative and stable,' but does not preview the quantitative contribution of each component; a short sentence or table reference in the abstract would help readers assess the central claim at first reading.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary of the paper, the recognition of its potential significance, and the recommendation for minor revision. No specific major comments were listed in the report.

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper presents PaW as an empirical co-training method that augments standard on-policy RL rollouts with auxiliary WM supervision through three explicitly described components (action-entropy data selection, noise-tolerant loss, reward-adaptive balancing). The central claim—that these rollouts supply usable (action, next-observation) pairs without altering inference—is validated by benchmark experiments rather than any derivation, equation, or prediction that reduces to fitted parameters or self-citations by construction. No self-definitional steps, fitted-input-as-prediction patterns, or load-bearing self-citation chains appear in the abstract or stated approach. The work is self-contained as a practical engineering proposal.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities can be extracted beyond the high-level domain assumption that RL transitions supply usable WM signal.

axioms (1)
  • domain assumption on-policy RL rollouts contain usable next-observation signal for world modeling
    Stated as the foundational observation enabling the method.

pith-pipeline@v0.9.1-grok · 5737 in / 1217 out tokens · 18717 ms · 2026-06-28T15:38:16.275849+00:00 · methodology

discussion (0)

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Reference graph

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