Tag: PPO
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High Discount Factor ฮณ=0.99 Causes Divergence: Fix Guide
Fix high discount factor ฮณ=0.99 divergence in RL with gradient clipping, reward scaling, and target networks. Practical debugging solutions.
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RLHF vs SFT: Why Supervised Fine-Tuning Wins 60% of Time
RLHF burned $50K before we admitted supervised fine-tuning worked better. Real cost, speed, and performance data from production LLM deployments.
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CleanRL vs Stable Baselines3: PPO Training 2.3x Faster
Compare CleanRL vs Stable Baselines3 PPO implementations and discover why CleanRL achieves 2.3x faster training with cleaner, hackable code.
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PPO vs SAC: 1-GPU Memory & Compute Cost Benchmark
SAC uses 40% more VRAM than PPO on the same taskโbut reaches target rewards 34% faster. Real benchmarks on RTX 3090 with memory and compute trade-offs.
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Stable Baselines3 VecEnv Reset Bug: 100K Step Desync Fix
Fix the Stable Baselines3 VecEnv reset bug causing 100K step desyncs. Learn why auto_reset breaks training and how to solve it properly.
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RLHF in 2026: Why Human Feedback Still Beats Pure AI Alignment
Explore RLHF in 2026 and discover why human feedback remains essential for AI alignmentโeven as models grow more capable. The surprising reasons inside.
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PPO vs SAC Sparse Rewards: 3x Sample Efficiency Gap
PPO vs SAC on sparse rewards: which RL algorithm learns faster? Benchmark shows 3x sample efficiency gap. Compare training curves and understand why.
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PPO Entropy Decay Bug: Why Exploration Dies at 500K Steps
Your PPO agent flatlines at 500K steps because entropy coefficient decay silently kills exploration. Here's the adaptive fix that saved my Ant-v4 runs.