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cloud.google.com·8h ago
AI Research
arXiv CS.AI·Aug 25·5 min read
Is Next-Chunk Reasoning RL Really Better than SFT? Revisiting Training Strategies under no-CoT Data
TL;DR
Next-chunk reasoning reinforcement learning (RL) was evaluated against a simpler mixed supervised fine-tuning (SFT) approach. Mixed SFT outperformed next-chunk reasoning RL while using significantly less computational resources.
✦ Why It Matters
Consider adopting mixed SFT for training models to improve efficiency and performance in reasoning tasks.
Key Takeaways
- Explore mixed SFT for better performance with less compute.
- Evaluate training strategies in the context of the full pipeline.
- Utilize no-CoT data effectively in model training.
- Understand that pre-training accuracy doesn't ensure post-training success.
How It Works
Next-chunk reasoning RL trains models to predict the next text segment based on implicit reasoning traces, rewarding correct predictions. Mixed SFT, on the other hand, combines no-CoT and long-CoT data in a single supervised training phase, allowing for a more comprehensive learning experience.