mlsanityData to 14 Oct 2026

Socratic RL: A Novel Framework for Efficient Knowledge Acquisition through Iterative Reflection and Viewpoint Distillation

arXiv 2506.13358 ↗First posted 16 Jun 2025cs.AI cs.LG cs.MAcited by 0

On the map: Knowledge distillation → On-policy and self-distillation for LLMs

Abstract (arXiv)

Current Reinforcement Learning (RL) methodologies for Large Language Models (LLMs) often rely on simplistic, outcome-based reward signals (e.g., final answer correctness), which limits the depth of learning from each interaction. This paper introduces Socratic Reinforcement Learning (Socratic-RL), a novel, process-oriented framework designed to address this limitation. Socratic-RL operates on the principle that deeper understanding is achieved by reflecting on the causal reasons for errors and successes within the reasoning process itself. The framework employs a decoupled "Teacher-Student" architecture, where a "Teacher AI" analyzes interaction histories, extracts causal insights, and formulates them into structured "viewpoints." These viewpoints, acting as distilled guidance, are then used by a "Student AI" to enhance its subsequent reasoning. A key innovation is the iterative self-improvement of the Teacher AI, enabling its reflective capabilities to evolve through a meta-learning loop. To manage the accumulation of knowledge, a distillation mechanism compresses learned viewpoints into the Student's parameters. By focusing on process rather than just outcome, Socratic-RL presents a pathway toward enhanced sample efficiency, superior interpretability, and a more scalable architecture for self-improving AI systems. This paper details the foundational concepts, formal mechanisms, synergies, challenges, and a concrete research roadmap for this proposed framework.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in On-policy and self-distillation for LLMs, one of 12 directions in Knowledge distillation. Builds on 18 of its 22 references that are in the map. First posted on arXiv 16 Jun 2025.

Builds on

18 of 22 references.

References · 18

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
  4. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  5. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  6. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
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  8. 2025Kimi k1.5: Scaling Reinforcement Learning with LLMscited by 1,210
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  10. 2025Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoningcited by 559
  11. 2025Open-Reasoner-Zero: An Open Source Approach to Scaling Up Reinforcement Learning on the Base Modelcited by 461
  12. 2025VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Taskscited by 285
6 more
  1. 2024RLHF Workflow: From Reward Modeling to Online RLHFcited by 267
  2. 2025A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforcecited by 145
  3. 2025Right Question is Already Half the Answer: Fully Unsupervised LLM Reasoning Incentivizationcited by 128
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  5. 2025SEED-GRPO: Semantic Entropy Enhanced GRPO for Uncertainty-Aware Policy Optimizationcited by 89
  6. 2025Not All Rollouts are Useful: Down-Sampling Rollouts in LLM Reinforcement Learningcited by 84

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  1. 2026Reinforcement-aware Knowledge Distillation for LLM Reasoning
  2. 2026SocraticPO: Policy Optimization via Interactive Guidance
  3. 2025KDRL: Post-Training Reasoning LLMs via Unified Knowledge Distillation and Reinforcement Learning
  4. 2024Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning
  5. 2025Beyond Accuracy: Dissecting Mathematical Reasoning for LLMs Under Reinforcement Learning
  6. 2025SAIL-RL: Guiding MLLMs in When and How to Think via Dual-Reward RL Tuning
  7. 2025A Survey of Reinforcement Learning for Large Reasoning Models
  8. 2026Distilled Reinforcement Learning for LLM Post-training