arxiv: 2605.08978 · v2 · submitted 2026-05-09 · 💻 cs.AI

Recognition: no theorem link

Learning to Explore: Scaling Agentic Reasoning via Exploration-Aware Policy Optimization

Ju Ren, Sheng Yue, Xingyuan Hua

Pith reviewed 2026-05-13 00:54 UTC · model grok-4.3

classification 💻 cs.AI

keywords LLM agentsreinforcement learningexplorationvariational inferencepolicy optimizationagentic reasoningtest-time scalingGUI agents

0 comments

The pith

LLM agents learn to explore selectively by estimating how actions reduce future uncertainty via variational rewards.

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

The paper claims that current LLM agents explore too broadly and without regard to whether more information is actually needed. It introduces a reward signal computed through variational inference that scores each exploratory step by how much it is expected to sharpen later decisions, plus a grouping step that trains exploratory and direct actions separately. This setup is meant to let agents gather feedback only under high uncertainty and then switch to task execution once the situation is clear enough. The result is shown as steady gains on both text-based and GUI agent tasks. If the approach holds, it would mean agent systems can scale their reasoning without proportional increases in wasted exploratory steps.

Core claim

The central claim is that an exploration-aware reinforcement learning framework enables LLM agents to adaptively explore only when uncertainty is high. This is achieved through a fine-grained reward function via variational inference that explicitly evaluates exploratory actions by estimating their potential to improve future decision-making, together with an exploration-aware grouping mechanism that separates exploratory actions from task-completion actions during optimization, allowing agents to target informational gaps and transition to execution as soon as the task context is clear, with consistent empirical improvements across text-based and GUI-based agent benchmarks.

What carries the argument

The fine-grained reward function obtained via variational inference that scores exploratory actions by their estimated effect on future decision quality, used together with an exploration-aware grouping mechanism during policy optimization.

If this is right

Agents achieve higher success rates on text-based and GUI-based benchmarks by limiting exploration to moments of genuine uncertainty.
The policy learns to transition from information-gathering to direct execution once task context becomes sufficiently clear.
Optimization no longer mixes exploratory and task-completion signals, allowing each type of action to be reinforced on its own terms.
Overall agent trajectories shorten because unneeded exploratory steps are avoided once the variational estimate indicates low remaining value.

Where Pith is reading between the lines

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

The same variational grouping idea could be tested in non-LLM agent settings such as robotic control or game environments where uncertainty estimation is also costly.
If the reward estimates remain reliable at longer horizons, the method might reduce total compute spent on failed long-horizon explorations in real deployments.
A natural next measurement is whether the learned policy generalizes to new task distributions without retraining the variational estimator.
The separation of exploration and execution signals may interact usefully with other intrinsic-motivation techniques already used in reinforcement learning.

Load-bearing premise

The variational inference step can produce unbiased estimates of how much an exploratory action will improve later decisions without itself depending on the very exploration it is trying to regulate.

What would settle it

Run the method on a held-out set of agent tasks while ablating the variational reward term; if performance gains disappear or exploratory actions no longer correlate with measured uncertainty reduction, the central mechanism is not working as claimed.

Figures

Figures reproduced from arXiv: 2605.08978 by Ju Ren, Sheng Yue, Xingyuan Hua.

**Figure 1.** Figure 1: Performance with varying model size. Uncertainty intervals depict standard deviation over three seeds. EAPO exhibits higher performance, demonstrating the effectiveness of exploration during test time and its great efficiency in encouraging agents to explore compared to existing methods. 0 200 400 600 800 1000 Steps 10 20 30 40 50 60 70 Success Rate ALFWorld 0 200 400 600 800 1000 Steps 10 20 30 40 50 60 W… view at source ↗

**Figure 2.** Figure 2: Training convergence with varying model size. Uncertainty intervals depict standard deviation over three seeds. EAPO consistently and significantly surpasses existing methods in terms of convergence speed and stability. step in Section D.7.7 and observe that our method indeed has additional runtime due to exploration. To avoid meanless exploration, we introduce introduce a discount factor γ so that our met… view at source ↗

**Figure 3.** Figure 3: Exploration degree comparison between EAPO and the alternative online reward. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_3.png] view at source ↗

**Figure 4.** Figure 4: Exploration degree comparison between EAPO and the alternative online reward. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_4.png] view at source ↗

**Figure 5.** Figure 5: System prompt template specifying the reasoning, exploration, memory, and action generation protocol. Basic Prompt for Instruction-Tuned Model You are an agent who can operate an Android phone on behalf of a user. Based on the user’s goal or request, you may: • Answer back if the request is a question or a chat message (e.g., “What is my schedule for today?”). • Complete tasks described in the request by p… view at source ↗

**Figure 6.** Figure 6: , and action guidance in [PITH_FULL_IMAGE:figures/full_fig_p020_6.png] view at source ↗

**Figure 7.** Figure 7: Action guidance specifying task objectives, action semantics, and usage examples for agentic models. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_7.png] view at source ↗

**Figure 8.** Figure 8: Performance with varying model size. Uncertainty intervals depict standard deviation over three seeds. EAPO exhibits higher performance, demonstrating the effectiveness of exploration during test time and its great efficiency in encouraging agents to explore compared to existing methods. 0 200 400 600 800 1000 10 20 30 40 50 60 70 Success Rate ALFworld 0 200 400 600 800 1000 10 20 30 40 50 60 WebShop 0 200… view at source ↗

**Figure 9.** Figure 9: Training convergence with varying model size. Uncertainty intervals depict standard deviation over three seeds. EAPO consistently and significantly surpasses existing methods in terms of convergence speed and stability. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_9.png] view at source ↗

**Figure 10.** Figure 10: Performance with varying discount γ when training Qwen models in text-based environments and Qwen-VL models in GUI-based environments. We further conduct experiment to verify the average steps when varying γ. As illustrated in Section D.4, the average number of steps consistently grows across all environments with increasing discount γ. This trend indicates that a larger γ encourages the agent to promote … view at source ↗

**Figure 11.** Figure 11: Performance with varying group size G when training Qwen models in text-based environments and Qwen-VL models in GUI-based environments. 27 [PITH_FULL_IMAGE:figures/full_fig_p027_11.png] view at source ↗

**Figure 12.** Figure 12: Performance with varying KL coefficient λ when training Qwen models in text-based environments and Qwen-VL models in GUI-based environments. 28 [PITH_FULL_IMAGE:figures/full_fig_p028_12.png] view at source ↗

**Figure 13.** Figure 13: The value of reward loss when training Qwen-1.7B in text-based environments and Qwen-VL-2B in GUI-based environments. D.7.2. CONVERGENCE OF POLICY MODEL We verify the convergence of policy updating by displaying the success rate by training steps. As shown in [PITH_FULL_IMAGE:figures/full_fig_p029_13.png] view at source ↗

**Figure 14.** Figure 14: The value of policy loss when training Qwen-1.7B in text-based environments and Qwen-VL-2B in GUI-based environments. D.7.3. REWARD We demonstrate how each part of the reward model (format, exploratory, and task success) changes during training in [PITH_FULL_IMAGE:figures/full_fig_p029_14.png] view at source ↗

**Figure 15.** Figure 15: The value of each part of the reward model when training Qwen-VL-2B in AndroidWorld. 29 [PITH_FULL_IMAGE:figures/full_fig_p029_15.png] view at source ↗

**Figure 16.** Figure 16: Caption 30 [PITH_FULL_IMAGE:figures/full_fig_p030_16.png] view at source ↗

**Figure 17.** Figure 17: Exploration degree with varying model size. EAPO exhibits increasing exploration degree at the beginning as it teaches agents to obtain dynamic information by exploration and converges at a certain level as it balances exploration and exploitation. 31 [PITH_FULL_IMAGE:figures/full_fig_p031_17.png] view at source ↗

**Figure 18.** Figure 18: Training convergence comparison between EAPO and ablating exploration-aware grouping. 33 [PITH_FULL_IMAGE:figures/full_fig_p033_18.png] view at source ↗

**Figure 19.** Figure 19: Exploration degree comparison between EAPO and ablating exploration-aware grouping. 34 [PITH_FULL_IMAGE:figures/full_fig_p034_19.png] view at source ↗

**Figure 20.** Figure 20: Runtime when varying the size of models. Uncertainty intervals depict standard deviation over three seeds. Further, we demonstrate the time cost of each component. As illustrated by [PITH_FULL_IMAGE:figures/full_fig_p035_20.png] view at source ↗

**Figure 21.** Figure 21: Runtime of each components when varying the size of models. 35 [PITH_FULL_IMAGE:figures/full_fig_p035_21.png] view at source ↗

**Figure 22.** Figure 22: Visualization of EAPO at step 1. 38 [PITH_FULL_IMAGE:figures/full_fig_p038_22.png] view at source ↗

**Figure 23.** Figure 23: Visualization of EAPO at step 2. 39 [PITH_FULL_IMAGE:figures/full_fig_p039_23.png] view at source ↗

**Figure 24.** Figure 24: Visualization of EAPO at step 3. 40 [PITH_FULL_IMAGE:figures/full_fig_p040_24.png] view at source ↗

**Figure 25.** Figure 25: Visualization of EAPO at step 4. 41 [PITH_FULL_IMAGE:figures/full_fig_p041_25.png] view at source ↗

**Figure 26.** Figure 26: Visualization of EAPO at step 5. The agent finds multiple possible actions and it decide to explore one by one. 42 [PITH_FULL_IMAGE:figures/full_fig_p042_26.png] view at source ↗

**Figure 27.** Figure 27: Visualization of EAPO at step 6. The agent realizes that it chooses the wrong action, memorze this state as additional information to understand the environment, and perform an action to rollback to the orginal state. 43 [PITH_FULL_IMAGE:figures/full_fig_p043_27.png] view at source ↗

**Figure 28.** Figure 28: Visualization of EAPO at step 7. With the additional information obtained from the exploration (step 5 and step 6), the agent becomes familiar with this unseen environment and notices the right place to click. 44 [PITH_FULL_IMAGE:figures/full_fig_p044_28.png] view at source ↗

**Figure 29.** Figure 29: Visualization of EAPO at step 8. 45 [PITH_FULL_IMAGE:figures/full_fig_p045_29.png] view at source ↗

**Figure 30.** Figure 30: Visualization of EAPO at step 9. 46 [PITH_FULL_IMAGE:figures/full_fig_p046_30.png] view at source ↗

read the original abstract

Recent advancements in agentic test-time scaling allow models to gather environmental feedback before committing to final actions. A key limitation of existing methods is that they typically employ undifferentiated exploration strategies, lacking the ability to adaptively distinguish when exploration is truly required. In this paper, we propose an exploration-aware reinforcement learning framework that enables LLM agents to adaptively explore only when uncertainty is high. Our method introduces a fine-grained reward function via variational inference that explicitly evaluates exploratory actions by estimating their potential to improve future decision-making, together with an exploration-aware grouping mechanism that separates exploratory actions from task-completion actions during optimization. By targeting informational gaps, this design allows agents to explore selectively and transition to execution as soon as the task context is clear. Empirically, we demonstrate that our approach achieves consistent improvements across a range of challenging text-based and GUI-based agent benchmarks. Code is available at https://github.com/HansenHua/EAPO-ICML26 and models are available at https://huggingface.co/hansenhua/EAPO-ICML26.

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.

Desk Editor's Note private letter to a colleague

The paper gives LLM agents a variational-inference reward that scores exploratory actions by their estimated future value, plus a grouping step to keep them separate during optimization, and reports gains on agent benchmarks.

read the letter

The paper's main contribution is a reward function built with variational inference that tries to quantify how much an exploratory action will help later decisions, combined with an explicit grouping mechanism that treats exploration steps differently from task-completion steps in the policy update. This is a direct attempt to make exploration conditional on uncertainty rather than applying it uniformly, which matches a real pain point in current agentic test-time scaling work. The authors show consistent improvements on text-based and GUI agent tasks and release both code and models, which makes the claims checkable in practice. That release is useful and raises the floor on what others can do with the idea. The framing against undifferentiated exploration baselines is clear and the high-level design is straightforward to follow. The soft spot sits in the variational inference construction itself. The reward depends on estimating the value of information-gathering actions, yet the abstract and high-level description do not spell out how the posterior is formed or whether the procedure can assign credit to exploration before the relevant trajectories have been collected. If the approximation under-values exploration in regimes where uncertainty is actually high, the policy could end up doing the opposite of what is intended. The grouping mechanism helps separate signals at optimization time, but it does not obviously solve the data-dependence issue inside the inference step. Without seeing the exact ELBO, the parametric assumptions, or ablations that isolate the VI component, it is difficult to judge how much of the reported gains trace to the new reward versus other training choices. This is aimed at researchers working on agentic LLMs, RL for reasoning, and test-time compute scaling. Readers who care about making exploration more selective will find the problem statement and the proposed levers worth examining, even if they end up implementing their own version after looking at the code. It deserves peer review because the problem is substantive, the method is concrete, and the public artifacts allow referees to test the claims directly rather than take them on trust.

Referee Report

2 major / 2 minor

Summary. The paper proposes an exploration-aware RL framework for LLM agents that adaptively explores only under high uncertainty. It introduces a fine-grained reward defined via variational inference that scores exploratory actions by their estimated improvement to future decision-making, paired with an exploration-aware grouping mechanism that separates exploratory actions from task-completion actions during policy optimization. The method is claimed to yield consistent gains on text-based and GUI-based agent benchmarks.

Significance. If the variational inference procedure can be shown to produce unbiased estimates of the value of information-gathering actions without circular dependence on the very exploratory trajectories it seeks to encourage, the approach would offer a principled way to scale agentic reasoning beyond undifferentiated exploration. The grouping mechanism and empirical results on challenging benchmarks would then constitute a meaningful contribution to test-time scaling for agents.

major comments (2)

[§3 (Method)] The central construction relies on a variational inference procedure to define the exploratory reward (abstract and §3). The skeptic correctly notes that the ELBO-style objective for estimating future improvement from an exploratory action typically requires either sufficient exploratory data or strong parametric assumptions; the manuscript does not demonstrate that the chosen variational family or data-collection policy satisfies this requirement, leaving open the possibility that the reward underestimates exploration precisely in the low-uncertainty regimes the method claims to handle adaptively.
[§4 (Optimization)] The exploration-aware grouping mechanism is presented as breaking the dependence between the VI objective and the policy being optimized (§4). However, the paper does not provide a formal argument or ablation showing that the separation prevents the variational posterior from inheriting bias from the current policy's limited exploration; without this, the claim that agents 'transition to execution as soon as the task context is clear' rests on an unverified assumption.

minor comments (2)

The abstract states that 'models are available at https://huggingface.co/hansenhua/EAPO-ICML26', but the manuscript does not specify the exact base model, training hyperparameters, or number of seeds used for the reported benchmark improvements.
[§3] Notation for the variational parameters and the grouping indicator variable is introduced without an explicit table or appendix listing all symbols, making it difficult to trace the reward definition through the optimization equations.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and insightful comments, which help clarify important aspects of our variational inference and grouping mechanisms. We address each major comment below and commit to revisions that strengthen the manuscript's theoretical and empirical grounding.

read point-by-point responses

Referee: [§3 (Method)] The central construction relies on a variational inference procedure to define the exploratory reward (abstract and §3). The skeptic correctly notes that the ELBO-style objective for estimating future improvement from an exploratory action typically requires either sufficient exploratory data or strong parametric assumptions; the manuscript does not demonstrate that the chosen variational family or data-collection policy satisfies this requirement, leaving open the possibility that the reward underestimates exploration precisely in the low-uncertainty regimes the method claims to handle adaptively.

Authors: We appreciate the referee's observation on the requirements for reliable ELBO-based estimation. Our method uses variational inference to score exploratory actions by their estimated value of information, with data collected under the evolving policy. While empirical results on benchmarks indicate effective adaptive behavior, we acknowledge the manuscript lacks explicit validation of the variational family and policy in low-uncertainty settings. We will revise §3 to discuss the assumptions underlying our variational approximation and add an ablation in §5 that tests reward estimation accuracy across controlled uncertainty levels with alternative variational families and data policies. This will directly address the potential for underestimation. revision: yes
Referee: [§4 (Optimization)] The exploration-aware grouping mechanism is presented as breaking the dependence between the VI objective and the policy being optimized (§4). However, the paper does not provide a formal argument or ablation showing that the separation prevents the variational posterior from inheriting bias from the current policy's limited exploration; without this, the claim that agents 'transition to execution as soon as the task context is clear' rests on an unverified assumption.

Authors: We agree that a formal argument and supporting ablation would strengthen the decoupling claim. The grouping mechanism separates exploratory trajectories (used for the VI reward) from task-completion trajectories during policy optimization, with the VI network trained on a distinct set of exploratory rollouts. To address the concern, we will add a concise formal sketch in §4 showing that the separation conditions the variational posterior solely on exploration-specific data collected via a behavior policy independent of the current policy's exploitation bias. We will also include an ablation in §5 comparing grouped versus joint optimization, quantifying bias reduction in the estimated rewards. These additions will better substantiate the adaptive transition to execution. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation is self-contained

full rationale

The abstract describes a reward function constructed via variational inference to score exploratory actions by their estimated future value, paired with a grouping mechanism for optimization. No equations, self-citations, or derivation steps are supplied that reduce this construction to its own inputs by definition, rename a fitted parameter as a prediction, or import uniqueness via author-overlapping citations. The variational procedure is presented as an independent modeling choice whose reliability is left to empirical demonstration rather than enforced by tautology. The overall framework therefore does not collapse into a self-referential loop on inspection of the given material.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated. The variational inference step likely introduces latent variables and optimization parameters whose values are fitted but not enumerated here.

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work page 2021
[29]

Table 3.Hyperparameters (identical across datasets). Hyperparameter Value Number of RL epochs 1000 Sampling group size 16 Weight of format rewardα 1 0.5 Weight of exploratory rewardα 2 1 Weight of Discount factorγ 0.9 Learning rate of reward model 1e-4 Learning rate of policy model 1e-4 KL loss coefficencyλ 0.01 We implement our code using Pytorch 2.8.0, ...

work page 2024
[30]

Specifically, the initial increase indicates that the agent can still benefit from short-term exploration when grouping is removed

We observe that, after ablating exploration-aware grouping, both the exploration degree and task performance exhibit a rise-then-fall trend during training. Specifically, the initial increase indicates that the agent can still benefit from short-term exploration when grouping is removed. However, as training progresses, exploratory actions and task-comple...

work page 2000
[31]

in average step. To be specific, we apply a discount to the exploratory gain since the benefit of exploration is not immediate – requiring at least one step to observe a new state and a subsequent step to synthesize the information. It guides the agent to carry out exploration only when the anticipated utility ‘outweighs’ the latency cost, which will avoi...

work page 2024