The Optimal Token Baseline: Variance Reduction for Long-Horizon LLM-RL
#Large Language Models #Reinforcement Learning #Gradient Variance #Token Baseline #Training Stability #arXiv #AI Research
📌 Key Takeaways
- Researchers developed a new 'Optimal Token Baseline' to solve the issue of training collapse in Large Language Models.
- The method targets exploding gradient variance, which typically destabilizes Reinforcement Learning in long-horizon tasks.
- Traditional value models and group-based baselines are criticized for failing to account for sequence heterogeneity.
- The new framework allows for more stable optimization, potentially improving the reasoning capabilities of generative AI.
📖 Full Retelling
🏷️ Themes
Artificial Intelligence, Machine Learning, Optimization
📚 Related People & Topics
Large language model
Type of machine learning model
A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the c...
Reinforcement learning
Field of machine learning
In machine learning and optimal control, reinforcement learning (RL) is concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learnin...
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- 🌐 Reinforcement learning (7 shared articles)
- 🌐 Machine learning (5 shared articles)
- 🌐 Theory of mind (2 shared articles)
- 🌐 Generative artificial intelligence (2 shared articles)
- 🌐 Automation (2 shared articles)
- 🌐 Rag (2 shared articles)
- 🌐 Scientific method (2 shared articles)
- 🌐 Mafia (disambiguation) (1 shared articles)
- 🌐 Robustness (1 shared articles)
- 🌐 Capture the flag (1 shared articles)
- 👤 Clinical Practice (1 shared articles)
- 🌐 Wearable computer (1 shared articles)
📄 Original Source Content
arXiv:2602.07078v1 Announce Type: cross Abstract: Reinforcement Learning (RL) for Large Language Models (LLMs) often suffers from training collapse in long-horizon tasks due to exploding gradient variance. To mitigate this, a baseline is commonly introduced for advantage computation; however, traditional value models remain difficult to optimize, and standard group-based baselines overlook sequence heterogeneity. Although classic optimal baseline theory can achieve global variance reduction, it