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Training & Fine-tuningMedium· Reinforcement Learning· Policy Optimization
How do you choose between different RL objectives like...
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See plansBoth Kimi K1.5 and DeepSeek R1 converge to similar structures—policy gradients scaled by baselined rewards with regularization. Empirical differences are largely driven by data quality and reward design rather than the specific objective. In practice, start with GRPO for familiarity, add a length reward after initial convergence, and remove standard deviation normalization if training instability is observed on near-uniform reward groups.