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About this paper
Senkang Hu, Yong Dai, Xudong Han, Zhengru Fang, Yuzhi Zhao, Sam Tak Wu Kwong, Yuguang Fang
The Self-Induced Outcome Potential (SIOP) framework provides a method for assigning turn-level credit to LLM agents in interactive environments without requiring explicit gold answers or task-specific verifiers. By clustering sampled final answers into semantic outcome modes and using reliability-calibrated potential differences to reward intermediate steps, SIOP improves performance over existing verifier-free methods and narrows the gap with gold-supervised techniques, particularly on multi-hop reasoning benchmarks.
Key insights
- Turn-level credit assignment can be effectively achieved without gold labels or external verifiers by leveraging the agent's own generated outputs to derive reliability-calibrated semantic outcome modes.
- A potential-based reward shaping mechanism, where rewards are differences in outcome potential, provides a principled way to offer dense, meaningful feedback at each intermediate step.
- Fine-grained token-level credit assignment within each turn, rather than broadcasting trajectory-level scores, is crucial for maximizing performance gains, especially in complex multi-hop reasoning tasks.
Summary by alphaXiv.
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