Challenges with Language Models
Large Language Models (LLMs) perform well in many tasks, but they struggle with multi-step reasoning, especially in complex scenarios like:
- Mathematical problem-solving
- Controlling embodied agents
- Web navigation
Current methods, such as Proximal Policy Optimization (PPO) and Direct Preference Optimization (DPO), are often costly and not effective enough for these tasks. There’s a clear need for better solutions.
Introducing OREO: Offline Reasoning Optimization
OREO (Offline REasoning Optimization) is a new solution to enhance the multi-step reasoning of LLMs.
- Developed by researchers from UC San Diego, Tsinghua University, Salesforce Research, and Northwestern University.
- Optimizes LLMs using a unique offline reinforcement learning approach.
- Allows use of unpaired datasets, improving efficiency.
- Enables precise credit assignment, crucial for tasks where few steps lead to success.
Key Features of OREO
- Simultaneously trains policy and value models through optimizing the soft Bellman Equation.
- Offers flexible objectives for various reasoning tasks.
- Implements advanced search techniques during testing, boosting accuracy.
- Learns from failures to improve robustness and adaptability.
Results and Performance
OREO has shown significant improvements in various benchmarks:
- 5.2% increase in accuracy on GSM8K compared to traditional methods.
- 10.5% improvement on the MATH dataset.
- 17.7% better performance in unseen environments on ALFWorld.
Iterative training enhances OREO’s effectiveness, continually improving its capabilities. Test-time search with OREO results in up to a 17.9% improvement in inference quality.
Conclusion
OREO is a powerful solution for enhancing reasoning in LLMs through offline RL. It addresses existing limitations, providing a viable method for tackling complex reasoning tasks. Its detailed credit assignment and iterative training make it suitable for various applications in AI.
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