Beginner Explanation
Imagine you’re learning to ride a bike. First, you practice balancing and pedaling in a safe area without traffic. This is like the initial training phase. Once you feel confident, you start riding on the street where you have to pay attention to cars and other cyclists. This is like the fine-tuning phase where you apply what you learned but adjust your skills to a more complex environment. The two-stage training strategy works similarly, first teaching a model the basics and then refining it for real-world challenges.Technical Explanation
The Two-Stage Training Strategy involves two distinct phases: the initial training phase and the fine-tuning phase. In the initial phase, a model is trained on a large dataset to learn general features. For example, using supervised learning with a loss function like cross-entropy. In the fine-tuning phase, the model is further trained using reinforcement learning, where it learns to maximize a reward signal based on its performance in a specific task. This can be implemented in Python using libraries like TensorFlow or PyTorch, where you first train a model with `model.fit()` and then use a reinforcement learning algorithm like Proximal Policy Optimization (PPO) to fine-tune it. Here’s a simplified code snippet: “`python # Initial training phase model.fit(training_data, training_labels) # Fine-tuning phase with reinforcement learning for episode in range(num_episodes): state = env.reset() done = False while not done: action = model.predict(state) next_state, reward, done, _ = env.step(action) model.learn(state, action, reward, next_state) state = next_state “`Academic Context
The Two-Stage Training Strategy is grounded in the principles of transfer learning and reinforcement learning. Initial training often leverages supervised learning techniques to establish a strong baseline model. The fine-tuning phase utilizes reinforcement learning algorithms, which have been extensively studied in literature, such as the seminal paper ‘Playing Atari with Deep Reinforcement Learning’ by Mnih et al. (2013). This approach allows models to adapt to dynamic environments by learning from the consequences of their actions, thereby improving their performance on specific tasks. The mathematical foundations involve Markov Decision Processes (MDPs) and the Bellman equation, which underpin the reinforcement learning framework.Code Examples
Example 1:
# Initial training phase
model.fit(training_data, training_labels)
# Fine-tuning phase with reinforcement learning
for episode in range(num_episodes):
state = env.reset()
done = False
while not done:
action = model.predict(state)
next_state, reward, done, _ = env.step(action)
model.learn(state, action, reward, next_state)
state = next_stateExample 2:
state = env.reset()
done = False
while not done:
action = model.predict(state)
next_state, reward, done, _ = env.step(action)
model.learn(state, action, reward, next_state)
state = next_stateView Source: https://arxiv.org/abs/2511.16666v1