Self-Induced Outcome Potential: Turn-Level Credit Assignment for Agents without Verifiers

Problem
This paper addresses the challenge of turn-level credit assignment in long-horizon language model (LLM) agents, particularly in scenarios where high-quality human annotations for intermediate feedback are unavailable. Existing methods either rely on answer supervision or stable task-specific verifiers, which limits their applicability. The authors propose a novel approach, Self-Induced Outcome Potential (SIOP), to facilitate credit assignment without requiring such verifiers, thereby filling a significant gap in the literature on reinforcement learning (RL) for LLMs.

Method
SIOP introduces a framework that leverages semantic clustering of final answers as latent future outcome states for potential-based credit assignment. The method operates as follows: for each query, SIOP samples multiple rollouts and clusters the final answers into semantic outcome modes. It constructs a reliability-aware target distribution over these states, allowing the model to reward intermediate turns based on their contribution to increasing posterior support for reliable future states. This is achieved through a tractable cluster-level approximation, which generalizes information-potential shaping from gold-answer supervision to settings devoid of task-specific verifiers. The authors formalize the framework and characterize its performance in relation to a supervised gold-answer limit.

Results
SIOP was evaluated across seven search-augmented agentic reasoning benchmarks, demonstrating significant improvements in average performance over existing verifier-free outcome-level baselines. Notably, SIOP approaches the performance of a gold-supervised outcome baseline, indicating its effectiveness in turn-level credit assignment. The paper reports specific effect sizes, although exact numerical results are not disclosed in the abstract.

Limitations
The authors acknowledge that SIOP’s reliance on clustering may introduce challenges in scenarios with high variability in answer semantics, potentially affecting the reliability of the target distribution. Additionally, the method’s performance may be sensitive to the quality of the clustering algorithm used. The paper does not address the computational overhead associated with sampling multiple rollouts, which could limit scalability in real-time applications. Furthermore, the generalizability of SIOP to tasks with highly diverse or ambiguous outcomes remains an open question.

Why it matters
The implications of SIOP are significant for the development of autonomous agents that require effective credit assignment mechanisms in the absence of human supervision. By enabling turn-level feedback without the need for task-specific verifiers, SIOP paves the way for more robust and adaptable LLMs in complex reasoning tasks. This work could inspire further research into unsupervised or semi-supervised learning paradigms in RL, particularly in environments where traditional supervision is impractical. The framework’s potential to generalize across various tasks may also lead to advancements in the efficiency and effectiveness of LLM training methodologies.

Authors: Senkang Hu, Yong Dai, Xudong Han, Zhengru Fang, Yuzhi Zhao, Sam Tak Wu Kwong, Yuguang Fang
Source: arXiv:2605.04984
URL: https://arxiv.org/abs/2605.04984v1