FutureWorld: A Live Reinforcement Learning Environment for Predictive Agents with Real-World Outcome Rewards

Chuyang Wei; Haoxiang Guan; Jian Li; Jiyan He; Kefei Chen; Maohang Gao; Mengting Hu; Shuxin Zheng; Xiawei Yue; Yanzhi Zhang

arxiv: 2604.26733 · v4 · pith:ZRRBVFRCnew · submitted 2026-04-29 · 💻 cs.AI · cs.LG

FutureWorld: A Live Reinforcement Learning Environment for Predictive Agents with Real-World Outcome Rewards

Zhixin Han , Yanzhi Zhang , Chuyang Wei , Maohang Gao , Xiawei Yue , Kefei Chen , Yu Zhuang , Haoxiang Guan

show 6 more authors

Jiyan He Jian Li Yitong Duan Yu Shi Mengting Hu Shuxin Zheng

This is my paper

Pith reviewed 2026-05-19 17:16 UTC · model grok-4.3

classification 💻 cs.AI cs.LG

keywords future predictionreinforcement learningdelayed rewardspredictive agentsreal-world outcomeslive environmentpolicy updatescalibration

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The pith

Delayed real-world outcome feedback serves as an effective reinforcement learning signal for predictive agents.

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

The paper introduces FutureWorld, a live reinforcement learning environment designed for agents that make predictions about unfolding real-world events. It stores prediction rollouts at the time they are made, waits for the actual outcomes to occur, backfills the corresponding rewards, and then replays the completed trajectories to update the agent's policy. Experiments across three open-source agents show consistent gains in prediction accuracy, probabilistic scoring, and calibration after successive training rounds. This setup demonstrates how delayed but grounded real-world feedback can close the learning loop without relying on immediate or simulated rewards. A sympathetic reader would care because it points toward agents that can keep improving by directly incorporating what actually happens next in the world.

Core claim

FutureWorld modifies verl-tool into verl-tool-future to store prediction-time rollouts, backfill rewards once real-world outcomes become available, and replay the completed trajectories for policy updates. Across three open-source agents, repeated training rounds produce measurable improvements in accuracy, scoring, and calibration, showing that delayed real-world outcome feedback functions as a usable reinforcement learning signal for live future prediction tasks.

What carries the argument

verl-tool-future framework that stores rollouts at prediction time and replays them with backfilled real-world rewards for policy updates.

If this is right

Agents can improve prediction quality by training directly on realized outcomes rather than proxy rewards.
Future event prediction supplies a scalable stream of grounded training questions that avoids answer leakage.
Policy updates become possible even when rewards arrive after the prediction is made.
Successive rounds produce better calibrated probability estimates alongside higher accuracy.
The approach turns live real-world events into a continual source of training data for agent systems.

Where Pith is reading between the lines

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

The same delayed-reward mechanism could be tested on forecasting tasks outside the current experiments, such as economic or scientific events.
If matching outcomes to predictions remains reliable at scale, the method could support longer-horizon agent training loops.
Extending the replay step to include additional context from the realized events might further strengthen updates.
This setup naturally lends itself to online, continually running agents that receive periodic batch updates.

Load-bearing premise

Real-world outcomes can be obtained, accurately matched to the original predictions, and converted into unbiased reward signals without major delays, selection effects, or data leakage.

What would settle it

Run the same agents through multiple FutureWorld rounds and observe no improvement or a decline in prediction accuracy and calibration metrics on a held-out set of future events.

Figures

Figures reproduced from arXiv: 2604.26733 by Chuyang Wei, Haoxiang Guan, Jian Li, Jiyan He, Kefei Chen, Maohang Gao, Mengting Hu, Shuxin Zheng, Xiawei Yue, Yanzhi Zhang, Yitong Duan, Yu Shi, Yu Zhuang, Zhixin Han.

**Figure 1.** Figure 1: Domain distributions of website sources (a), questions before resampling (b), and questions view at source ↗

**Figure 2.** Figure 2: Overview of the FutureWorld pipeline for constructing prediction questions. view at source ↗

**Figure 3.** Figure 3: Overview of the FutureWorld training loop. view at source ↗

**Figure 4.** Figure 4: Prediction performance across model checkpoints saved on different days. Shaded regions view at source ↗

**Figure 5.** Figure 5: Prediction performance across model checkpoints saved on different days. Shaded regions view at source ↗

**Figure 6.** Figure 6: Effect of scaling the number of daily prediction questions on view at source ↗

**Figure 7.** Figure 7: Daily overall scores of frontier agents on the FutureWorld daily benchmark over four view at source ↗

read the original abstract

Live future prediction refers to the task of making predictions about real-world events before they unfold. This task is increasingly studied using large language model-based agent systems, and it is important for building agents that can continually learn from the real world. It can provide a large number of prediction questions grounded in diverse real-world events, while preventing answer leakage. To leverage the advantages of future prediction, we present FutureWorld, a live agentic reinforcement learning environment that closes the training loop between prediction, outcome realization, and parameter updates. Specifically, we modify and extend verl-tool, resulting in a new framework that we call verl-tool-future. Unlike standard reinforcement learning training frameworks that rely on immediate rewards, verl-tool-future stores prediction-time rollouts, backfills rewards after real-world outcomes become available, and then replays the completed trajectories for policy update. Across three open-source agents, successive FutureWorld training rounds lead to consistent improvements in prediction accuracy, probabilistic scoring, and calibration, demonstrating that delayed real-world outcome feedback can serve as an effective reinforcement learning signal.

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 sketches a delayed-reward replay loop for live prediction agents but supplies almost no numbers or bias corrections, so the claimed gains are hard to evaluate.

read the letter

The main thing to know is that FutureWorld tries to turn real-world outcomes into a training signal for predictive agents by storing rollouts at prediction time, attaching rewards once events resolve, and replaying the trajectories later. They built this on top of verl-tool as verl-tool-future and ran it on three open-source agents, reporting better accuracy, scoring, and calibration after successive rounds. That framing is the concrete new piece: it closes a loop with external, leakage-free feedback instead of simulated rewards. The setup makes sense for anyone who wants agents to keep learning from ongoing events rather than fixed datasets. It does a decent job laying out the basic flow and why delayed outcomes could work as an RL signal. The practical angle is useful if the goal is continual adaptation in forecasting tasks. The soft spots are bigger than minor. The writeup gives no quantitative results, no baselines, no error bars, and no statistical details, so there is no way to judge whether the improvements are real, large, or stable. The off-policy issue raised in the stress test is also unaddressed: if the policy changes between storing a trajectory and backfilling its reward, replaying it with standard on-policy methods can produce biased updates, yet the description mentions no importance sampling or other fix. Accurate matching of predictions to outcomes and avoiding selection effects are assumed without much explanation of how they are handled in practice. This is for researchers working on agentic systems and real-world RL loops. A reader who wants to experiment with live feedback might get some implementation ideas from the framework, but they would need the full methods, code, and actual numbers to make use of it. It deserves peer review because the problem is relevant and the basic mechanism is workable, even though the current version needs substantial additions on results and bias handling before it can be assessed properly.

Referee Report

2 major / 1 minor

Summary. The paper introduces FutureWorld, a live reinforcement learning environment extending verl-tool to verl-tool-future for predictive agents. It stores prediction-time rollouts, backfills real-world outcome rewards after delays, and replays completed trajectories for policy updates. The central empirical claim is that successive training rounds produce consistent gains in prediction accuracy, probabilistic scoring, and calibration across three open-source agents, establishing delayed real-world feedback as an effective RL signal.

Significance. If the reported improvements prove robust after addressing off-policy concerns and providing quantitative validation, the framework could meaningfully advance continual learning for grounded prediction agents by closing the loop with real-world delayed outcomes and avoiding static-dataset leakage.

major comments (2)

[verl-tool-future framework description] In the description of the verl-tool-future framework, trajectories are stored at prediction time and replayed after backfilling rewards from later real-world outcomes. Because training proceeds in successive rounds, intervening policy updates render these trajectories off-policy for standard on-policy methods (PPO, GRPO) referenced in the verl-tool base. No importance-sampling corrections or other adjustments are mentioned, which directly undermines the claim that observed gains demonstrate effective reinforcement from delayed outcomes rather than artifacts of stale data.
[Abstract] The abstract asserts 'consistent improvements' in accuracy, scoring, and calibration across three agents, yet supplies no quantitative results, baselines, statistical tests, error bars, or implementation details. This absence makes it impossible to assess whether the data support the central claim that delayed feedback serves as an effective RL signal.

minor comments (1)

The workflow diagram or pseudocode for the store-backfill-replay loop would clarify how delays and batching are managed.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed comments on our manuscript. We address each major comment point by point below, indicating the revisions we will make to strengthen the paper.

read point-by-point responses

Referee: In the description of the verl-tool-future framework, trajectories are stored at prediction time and replayed after backfilling rewards from later real-world outcomes. Because training proceeds in successive rounds, intervening policy updates render these trajectories off-policy for standard on-policy methods (PPO, GRPO) referenced in the verl-tool base. No importance-sampling corrections or other adjustments are mentioned, which directly undermines the claim that observed gains demonstrate effective reinforcement from delayed outcomes rather than artifacts of stale data.

Authors: We agree that the off-policy character of the replayed trajectories due to intervening policy updates is a substantive issue not addressed in the current manuscript. The description of verl-tool-future does not mention importance sampling or other corrections for the distribution shift between prediction-time rollouts and later policy updates. In the revised manuscript we will add an explicit discussion of this point in the framework section and incorporate importance-sampling corrections into the training procedure for completed trajectories. These changes will clarify that the reported gains are obtained under properly adjusted off-policy updates rather than unadjusted replay of stale data. revision: yes
Referee: The abstract asserts 'consistent improvements' in accuracy, scoring, and calibration across three agents, yet supplies no quantitative results, baselines, statistical tests, error bars, or implementation details. This absence makes it impossible to assess whether the data support the central claim that delayed feedback serves as an effective RL signal.

Authors: The abstract is written as a concise summary of the contribution and high-level findings. All requested quantitative elements—specific accuracy, scoring, and calibration improvements, baseline comparisons, statistical tests, error bars, and implementation details—are reported in Section 4 of the full manuscript. To directly address the referee’s concern we will revise the abstract to include a short quantitative statement summarizing the magnitude of gains across the three models while remaining within length constraints. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical results rely on external real-world outcomes

full rationale

The paper introduces verl-tool-future to store rollouts at prediction time, attach delayed real-world rewards, and replay for updates. Its central claim consists solely of observed improvements in accuracy, probabilistic scoring, and calibration across three agents after successive rounds. No equations, derivations, fitted parameters renamed as predictions, or self-referential definitions appear in the provided text. The demonstration is therefore self-contained against external benchmarks and does not reduce any result to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim depends on the feasibility of obtaining and aligning real-world outcomes with stored predictions and on the correctness of the delayed-reward replay mechanism; no free parameters or invented physical entities are mentioned.

axioms (1)

domain assumption Real-world outcomes can be reliably obtained and matched to specific predictions without introducing bias or leakage.
The backfill-and-replay process requires accurate, timely outcome data to produce valid rewards.

invented entities (1)

verl-tool-future no independent evidence
purpose: Framework extension that stores rollouts and backfills delayed rewards for policy updates.
New software component introduced to implement the live training loop.

pith-pipeline@v0.9.0 · 5760 in / 1402 out tokens · 57327 ms · 2026-05-19T17:16:19.817368+00:00 · methodology

Review history (4 revisions) →

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

verl-tool-future stores prediction-time rollouts, backfills rewards after real-world outcomes become available, and then replays the completed trajectories for policy update
IndisputableMonolith/Foundation/BranchSelection.lean branch_selection unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

reward rq,k = −(π̂q,k − zq)² (negative Brier)

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

3 extracted references · 3 canonical work pages · 1 internal anchor

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[1] [1]

URLhttps://arxiv.org/abs/2502.01600. Tri Dao. Flashattention-2: Faster attention with better parallelism and work partitioning, 2023. URL https://arxiv.org/abs/2307.08691. DeepSeek-AI. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning,

work page arXiv 2023

[2] [2]

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

URLhttps://arxiv.org/abs/2501.12948. Alexandre Drouin, Maxime Gasse, Massimo Caccia, Issam H. Laradji, Manuel Del Verme, Tom Marty, Léo Boisvert, Megh Thakkar, Quentin Cappart, David Vazquez, Nicolas Chapados, and Alexandre Lacoste. Workarena: How capable are web agents at solving common knowledge work tasks?, 2024. URLhttps://arxiv.org/abs/2403.07718. Ch...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.1609/aaai.v34i05.6297 2024

[3] [3]

VisualWebArena: Evaluating multimodal agents on realistic visual web tasks

URLhttps://arxiv.org/abs/2005.00792. 14 Ezra Karger, Houtan Bastani, Chen Yueh-Han, Zachary Jacobs, Danny Halawi, Fred Zhang, and Philip E. Tetlock. Forecastbench: A dynamic benchmark of ai forecasting capabilities, 2025. URL https://arxiv.org/abs/2409.19839. Jing Yu Koh, Robert Lo, Lawrence Jang, Vikram Duvvur, Ming Lim, Po-Yu Huang, Graham Neubig, Shuya...

work page doi:10.18653/v1/2024.acl-long.50 2005