Experiential Reinforcement Learning (Feb 2026)

Описание: Title: Experiential Reinforcement Learning (Feb 2026)
Link: http://arxiv.org/abs/2602.13949v1
Date: February 2026

Summary:
The paper introduces Experiential Reinforcement Learning (ERL), a training paradigm that incorporates an experience–reflection–consolidation loop into the reinforcement learning process. ERL allows language models to generate initial attempts, receive feedback, and produce verbal reflections to guide subsequent refined attempts. These successful behavioral corrections are then internalized into the base policy through self-distillation, enabling the model to achieve significant performance gains in sparse-reward environments and reasoning tasks without additional inference costs.

Key Topics:
Experiential Reinforcement Learning
Large Language Models
Self-Reflection
Policy Internalization
Sparse-Reward Environments
Agentic Reasoning

Chapters:
00:00 - ERL Paper Snapshot
01:38 - Navigating The RL Landscape
02:50 - Solving Credit Assignment Problems
04:18 - Analyzing Sokoban Benchmark Torture
05:58 - Designing The Reflection Loop
07:51 - Managing Cross-Episode Memory
09:14 - Internalizing Reasoning Into Weights
10:48 - Evaluating Sparse Reward Results
12:29 - Optimizing Training Sample Efficiency
14:19 - Reviewing Ablation Study Insights
16:16 - Tracking The Internalization Curve
17:43 - Comparing ERL With R1
19:16 - Balancing Speed And Interpretability
20:44 - Scaling Toward Artificial Intuition

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