Why use two networks when one does the job better? REINFORCE++ proves it
REINFORCE++ cuts LLM alignment training time by 30% while maintaining performance
Token-level KL penalties + PPO-clip loss = Better LLM alignment without the overhead
Simplifies the REINFORCE algorithm while removing the critic network, achieving better stability and computational efficiency.
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https://arxiv.org/abs/2501.03262
Original Problem 🤔:
Existing LLM alignment methods like PPO require complex critic networks and face stability issues. GRPO and other approaches introduce additional complexities that can lead to training instability.
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Solution in this Paper 🛠️:
→ REINFORCE++ builds on the classical REINFORCE algorithm by incorporating PPO's optimization techniques without needing a critic network
→ It implements token-level KL penalties between RL and SFT model distributions for better credit assignment
→ The solution uses PPO-clip loss to constrain policy updates while maintaining stability
→ Mini-batch processing with multiple parameter updates improves convergence rates
→ Comprehensive reward normalization and clipping stabilize training dynamics
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Key Insights 💡:
→ Critic networks in PPO create unnecessary computational overhead
→ Token-level KL penalties prevent reward and length hacking effectively
→ Z-score normalization of advantages ensures stable gradients
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Results 📊:
→ Training time reduced from 60 to 42 hours on NVIDIA H100 with LLaMA3 8b model
→ Better stability than GRPO in preventing reward/length hacking
→ Comparable performance to GRPO with group normalization in rule-based scenarios
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