Metrik model dan penyesuaian

Reinforcement Learning from Human Feedback (RLHF)

Mina Parham

AI Engineer

Mengapa gunakan model referensi?

  • Output tidak bermakna

Serangkaian emoji.

Reinforcement Learning from Human Feedback (RLHF)

Memeriksa output model

Diagram proses RLHF dengan pemeriksaan respons.

Reinforcement Learning from Human Feedback (RLHF)

Solusi: divergensi KL

Diagram proses RLHF dengan divergensi KL.

Reinforcement Learning from Human Feedback (RLHF)

Solusi: divergensi KL

  • Tambahkan penalti ke model reward
  • Penalti mengoreksi arah saat output tidak relevan
  • Divergensi KL membandingkan model saat ini dan model reward

Ikon timbangan.

  • Antara 0 dan 10, dan tidak pernah negatif
Reinforcement Learning from Human Feedback (RLHF)

Menyesuaikan parameter

generation_kwargs = {

    "min_length": -1, # don't ignore the EOS token

    "top_k": 0.0, # no top-k sampling

    "top_p": 1.0, 
    "do_sample": True, 
    "pad_token_id": tokenizer.eos_token_id, 
    "max_new_tokens": 32}

 

  • Parameter diteruskan ke model kebijakan
Reinforcement Learning from Human Feedback (RLHF)

Memeriksa model reward

  • Memeriksa model reward

  • Memeriksa output (reward)

reward_model_results.head()

|ID | Comment                                     |Sentiment |Reward|
|---|---------------------------------------------|----------|------|
| 1 | This event was lit! So much fun!            | Positive |  0.9 |
| 2 | Terrible experience, never attending again. | Negative | -0.8 |
| 3 | It was okay, nothing extraordinary.         | Neutral  |  0.2 |
| 4 | The event was poorly organized and chaotic. | Negative | -0.85|
| 5 | Had an amazing time with great people!      | Positive |  0.95|
Reinforcement Learning from Human Feedback (RLHF)

Memeriksa model reward

  • 👍 👎 Periksa kasus ekstrem
    extreme_positive = reward_model_results[reward_model_results['Reward'] >= 0.9]
extreme_negative = reward_model_results[reward_model_results['Reward'] <= -0.8]

  • 🧘 Pastikan dataset seimbang

    sentiment_distribution = reward_model_results['Sentiment'].value_counts()

  • 📊 Normalisasi model reward

    from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(-1, 1))
scaler.fit_transform(reward_model_results[['Reward']])
Reinforcement Learning from Human Feedback (RLHF)

Ayo berlatih!

Reinforcement Learning from Human Feedback (RLHF)

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