DeepSeek R1: Under the hood & lessons for AI in India

Introduction

The discussion centered around DeepSeek R1, its reinforcement learning (RL) training methodology, and the implications of its approach on AI development. Harshad Saykhedkar provided insights into DeepSeek’s core mechanics, lessons learned from experimentation, and takeaways for India’s AI ecosystem.

Understanding DeepSeek R1’s Training Approach

Reinforcement Learning (RL) in AI

DeepSeek R1 employs reinforcement learning (RL), where models learn through trial and error, receiving rewards for desirable behaviors. This technique is crucial for fine-tuning language models, helping them improve based on human or AI feedback.

Core mechanics of DeepSeek’s RL process

DeepSeek R1’s RL training follows these key steps:

1. Pretraining Phase – The model undergoes supervised training with large-scale internet text.
2. Reward Model Development – A separate model is trained to assess and score responses.
3. Proximal Policy Optimization (PPO) – An RL algorithm optimizes the model iteratively to maximize rewards.
4. Human Alignment – RLHF (Reinforcement Learning from Human Feedback) aligns responses to human preferences.

Comparison with OpenAI and Anthropic models

DeepSeek R1’s methodology is similar to OpenAI’s GPT and Anthropic’s Claude, but with a stronger emphasis on open source accessibility, allowing researchers and developers to build upon their findings.

Challenges and Lessons Learned from Replicating DeepSeek’s RL

Computational costs

RL training requires vast GPU resources, making large-scale RLHF experiments expensive.

Scaling issues

DeepSeek R1 shows that RL scales well but is inefficient for smaller models, posing a challenge for startups and research teams with limited resources.

Overfitting in RL models

A major issue is overfitting to the reward model — if biases exist in the reward system, the AI model inherits them, leading to skewed results.

Strategies to improve RL training

1. Diverse reward models – using multiple reward systems to balance biases.
2. Mixing Supervised and RL Training – pretraining on human-labeled data before RL fine-tuning.
3. Data augmentation – Introducing varied datasets to improve generalization.

Key takeaways for India’s AI ecosystem

Need for compute infrastructure

India’s AI sector lacks large-scale GPU clusters, which are essential for RL training.

Importance of open source AI models

DeepSeek’s success highlights the power of open-source AI, reducing dependency on proprietary models.

Bridging the talent gap

India needs more AI specialists trained in RLHF and reward modeling to stay competitive.

Funding and government support

High RL training costs suggest a need for government and private investment to support AI research.

Q&A and audience discussions

Can RL-based training improve Indian LLMs?

Yes, but access to high-quality human feedback data is a major challenge.

What are the risks of RL training?

Concerns include model collapse, where excessive optimization leads to poor generalization.

How Can start-ups leverage RL training without high costs?

• Using pre-trained models with lightweight RL fine-tuning.
• Accessing cloud-based GPU solutions.
• Collaborating with research institutions to share resources.

Conclusion

DeepSeek R1’s RL training process demonstrates the potential of RLHF in AI fine-tuning, while also revealing challenges in compute availability, model alignment, and cost-effectiveness. The discussion emphasized the need for:

• Better AI infrastructure
• Stronger open source collaboration
• Skilled reinforcement learning talent in India