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UnifiedPlayers : améliorez le raisonnement intégré aux outils dans l'apprentissage par renforcement agent

UnifiedPlayers est un cadre coopératif qui permet aux agents utilisant des outils de générer leurs propres données de formation, réduisant ainsi le besoin de trajectoires annotées par l'homme.

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Source-provided image accompanying UnifiedPlayers: Enhance Tool-Integrated Reasoning in Agentic Reinforcement Learning
Document de source principaleSource enregistrée
Éditeur
arxiv.org
Lien source
arxiv.orghttps://arxiv.org/abs/2609.20089
Type de source
Document principal : une annonce officielle, un document, un dépôt ou une page de première partie que nous lisons directement.
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Termes clés

Apprentissage par renforcement
La formation par récompense signale qu'un agent apprend des actions qui maximisent le rendement à long terme.
Intelligence artificielle (IA)
Le vaste domaine de la construction de systèmes qui exécutent des tâches nécessitant une reconnaissance de formes, un raisonnement, un langage ou une prise de décision.
Apprentissage automatique (ML)
Méthodes qui permettent aux systèmes d’apprendre des modèles à partir des données et de s’améliorer au fil du temps.
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Que s'est-il passé

Researchers introduced UnifiedPlayers, a cooperative framework that addresses the coordination challenge in jointly adapting planning, execution, and evaluation in tool-integrated agents. UnifiedPlayers comprises a Planning Player, an Execution Player, and an Evaluation Player, which work together to generate tasks, produce multi-turn trajectories, and construct executable verifiers.

UnifiedPlayers is a cooperative framework that addresses the coordination challenge in jointly adapting planning, execution, and evaluation in tool-integrated agents.

The framework comprises a Planning Player, an Execution Player, and an Evaluation Player, which work together to generate tasks, produce multi-turn trajectories, and construct executable verifiers.

UnifiedPlayers outperforms the strongest prior baseline by at least 3.5% on mathematical reasoning and 3.9% on general reasoning tasks.

The learned verifier achieves 84.2% adversarial detection accuracy, while its reward signal exhibits 2.03$ imes$ higher per-question variance than a self-consistency baseline.

Détails de la source: arxiv.org ↗

Pourquoi c'est important

UnifiedPlayers offers a promising path toward self-enhanced tool-integrated agents, which can improve reasoning and decision-making capabilities. The framework's ability to adapt to emerging failure modes and self-consistency signals can lead to more accurate and reliable agents.

UnifiedPlayers offers a promising path toward self-enhanced tool-integrated agents, which can improve reasoning and decision-making capabilities.

The framework's ability to adapt to emerging failure modes and self-consistency signals can lead to more accurate and reliable agents.

The development of UnifiedPlayers has the potential to impact various fields, including artificial intelligence, machine learning, and robotics.

Interactive Mechanism

Mécanisme interactif : comment cela fonctionne réellement

Explorez de manière interactive la technologie sous-jacente à ce développement.

Thinking Budget (Test-Time Tokens):1,024 tokens
Complex Accuracy79%Math & Code Logic
Latency3.2sTime to first full output
Inference Cost$0.0092Per query estimated
Reasoning StyleStep VerificationInternal chain depth
Active Thinking Trace:
1Deconstruct user problem into formal constraints
2Propose candidate hypotheses & step-by-step calculation
3Self-correction: Backtrack and refute subtle edge cases
4Exhaustive consistency check & final output synthesis
Core takeaway: Test-time compute fundamentally changes AI economics. Instead of only scaling during pre-training, giving reasoning models more tokens at inference time allows them to systematically solve PhD-level STEM problems.
Vérification de concept interactive+10 Points
What is AI? Quiz

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Que regarder ensuite

The development of UnifiedPlayers has the potential to impact various fields, including artificial intelligence, machine learning, and robotics. The framework's ability to improve tool-integrated agents can lead to more efficient and effective decision-making processes.

The impact of UnifiedPlayers on tool-integrated agents and their applications in various fields.

The potential of UnifiedPlayers to improve reasoning and decision-making capabilities in artificial intelligence and machine learning.

The development of UnifiedPlayers and its potential to lead to more efficient and effective decision-making processes.

Guides et quiz associés

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