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Evidence record 4045 · automatically gathered

LC-ERD: Mining Latent Logic for Self-Evolving Reasoning via Consistency-Regulated Reward Decomposition

The evolution of Large Language Model (LLM) reasoning is bottlenecked by the scarcity of high-quality process data. While self-alignment via endogenous rewards offers a solution, mining valid supervision faces three challenges: (1) Label Noise via Mimetic Bias, where rewards prioritize statistical likelihood over logical truth, creating a "correctness illusion" that masks compounding errors; (2) Coarse-Grained Supervision, where sparse global outcomes (e.g., in GRPO) fail to provide granular gui

Record details

Published: 19 May 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Regulation · Safety & alignment
Retrieved: 14 July 2026

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These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (19 May 2026), “LC-ERD: Mining Latent Logic for Self-Evolving Reasoning via Consistency-Regulated Reward Decomposition,” evidence record 4045, https://ethics.ai/record/4045 (originally published by arXiv).

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