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OpenAI

9 problems tagged for OpenAI.

Easy× 1Medium× 5Hard× 2Expert× 1How these tags are sourced

Implement Temperature Sampling

Divide logits by a temperature scalar before softmax to sharpen or flatten the token probability distribution.

Easy

Implement Contrastive Loss (InfoNCE) + CLIP Training Loop

Build the InfoNCE contrastive loss and a CLIP-style training loop that aligns image and text embeddings.

Medium

Implement KV Cache for Autoregressive Generation

Cache key and value tensors from previous timesteps so each new token only computes attention over one new position.

Medium

Implement LoRA on a Linear Layer

Inject trainable low-rank decomposition matrices (A, B) into a frozen linear layer for parameter-efficient fine-tuning.

Medium

Implement Top-k Sampling

Select the k highest-probability tokens, zero out the rest, renormalize, and sample for controlled text generation.

Medium

Implement Top-p (Nucleus) Sampling

Sort logits, compute cumulative probabilities, mask tokens below the nucleus threshold, and sample from the filtered distribution.

Medium

Implement DPO Loss from Scratch

Compute the Direct Preference Optimization loss that trains a policy directly from preference pairs without a reward model.

Hard

Implement PPO for RLHF

Build Proximal Policy Optimization with clipped surrogate objective, value function baseline, and KL penalty for RLHF.

Hard

Implement GRPO (DeepSeek-R1 Algorithm)

Build Group Relative Policy Optimization that scores multiple completions per prompt and uses group-relative advantages.

Expert