LC-ERD: Mining Latent Logic for Self-Evolving Reasoning via Consistency-Regulated Reward Decomposition
arXiv:2605.24005v1 Announce Type: cross Abstract: 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 guidance, treating reasoning chains as monolithic; an
The rapid advancement of LLMs has exposed the limitations of current training methods, making the refinement of autonomous reasoning a critical bottleneck that requires innovative solutions now.
Improving LLM reasoning through methods like LC-ERD is crucial for developing truly intelligent AI agents, enhancing their capabilities beyond current limitations and enabling more complex autonomous functions.
This research introduces methods to overcome key challenges in LLM self-alignment, potentially leading to more robust, logically sound, and less error-prone AI systems, thereby accelerating the development of advanced AI agents.
- · AI research labs
- · Developers of AI agents
- · Companies relying on advanced LLM reasoning
- · AI models with brittle reasoning
- · Current manual data labeling processes
- · Developers of narrow AI tools
Refined reward decomposition techniques enhance the self-correction and reasoning abilities of LLMs.
AI agents become more capable of complex, multi-step problem-solving without human intervention, expanding their utility.
The increased reliability and autonomy of AI systems could lead to widespread integration into critical infrastructure and decision-making processes, redefining human-computer interaction paradigms.
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Read at arXiv cs.CL