# CONCEPTUAL PRE-TRAINING LOOP
internet_documents = crawl_internet()

internet_documents.each do |doc|
  # 1. Tokenize the entire document into an array of integers
  # In reality, this is done in chunks since the docs are probably too big. 
  # ATFLLM (Ask The F.. LLM) to learn about different chunk sizes.
  tokens = tokenizer.encode(doc) 

  # Inputs are tokens 0 to N-1
  # ["The", "capital", "of", "France", "is", "Paris", "and", "its", "population", "is", "about", "2", ...] 
  inputs = tokens[0...-1] 

  # Labels are tokens 1 to N (The "Answer" is just the next token)
  # ["capital", "of", "France", "is", "Paris", "and", "its", "population", "is", "about", "2", "million", ....]
  labels = tokens[1..-1] 

  # 2. THE FORWARD PASS
  # The model predicts the probability for EVERY token in the sequence at once 
  # (this is magic that GPUs provide since they are embarrassingly parallel) 
  predictions = model.predict(inputs)

  # 3. THE LOSS (The "Grade")
  # We compare the predicted probability of the 'correct' next token 
  # to the actual token that was in the text. 
  # Lower number is better.
  loss = calculate_loss(predictions, labels)

  # 4. THE BACKWARDS UPDATE
  # Nudge the weights prior to this so that (for example) 
  # 'is' predicts 'about' more strongly next time.
  model.adjust_weights(loss)
end

def generate_thoughts(prompt, number_of_samples=10)
    trajectories = []
    n_samples.times do
        thought_trace = model.generate(prompt,
                                       max_tokens: 8192,
                                       temperature: 0.8)
        trajectories << { thoughts: thought_trace.thoughts, answer: thought_trace.answer }
    end
    return trajectories
end

# Process-Supervised Training (Thinking)
def train_thinking_step(prompt, ground_truth_answer)
  # 1. Generate a "Thought Trace" (Chain of Thought)
  thought_trace = model.generate_thoughts(prompt)
  
  # 2. EVALUATE THE PROCESS (The "Secret Sauce")
  # We use a Process Reward Model (PRM) to grade each step
  step_rewards = thought_trace.map do |step|
    process_reward_model.grade(step) # Is this step logical?
  end
  
  # 3. EVALUATE THE OUTCOME
  final_answer = model.generate_final_answer(prompt, thought_trace)
  outcome_reward = (final_answer == ground_truth_answer ? 1.0 : 0.0)

  # 4. REINFORCEMENT LEARNING
  # We update the weights based on the SUM of the step rewards
  total_reward = step_rewards.sum + outcome_reward
  model.update_weights_via_ppo(total_reward)
end

def generate_with_cot(prompt, number_of_samples=10)
    trajectories = []
  
    number_of_samples.times do
        # We sample with Temperature > 0 to encourage exploration
        # Higher values create more hallucinations as it starts to 
        # drift further and further from the training data.
        thought_trace = model.generate( prompt, 
                                        max_tokens=8192,
                                        temperature: 0.8 
                        )
        
        trajectories << { thoughts: thought_trace.thoughts, answer: thought_trace.answer }
    end
    
    return trajectories
end

def calculate_reward(thought_trace, final_answer, correct_answer, thought_complexity)
  # Step 1: RL VR 
  # If it's wrong we need to negatively reinforce this path
  return -1.0 unless final_answer == correct_answer
  
  # Base reward for being right
  reward = 1.0 
  
  # Step 2: Evaluation via Process Reward Model
  helpfulness, correctness, coherence, complexity, verbosity = process_reward_model.evaluate(thought_trace)
  # Did it actually 'think' or just guess?
  # Various models might tune these all differently
  reward += 0.2 if helpfulness > 0.5 && coherence > 0.5
  reward -= 0.2 if correctness < 0.5 || complexity > 0.5
  # And so on
      
  # Token efficiency check: 
  # If the thought trace is 10,000 words for a 2+2 problem, penalize it.
  reward -= 0.2 if verbosity > 0.5
      
  return reward
end

def calculate_loss_for_tool_syntax(response, ideal_response)
  reward = 0.0

  # This is handwavy to keep the code simple
  # We needed a tool, they didn't get it
  # Could still be helpful so don't kill it entirely
  reward -= 0.5 if response.didnt_use_tool?

  # Picked the wrong tool - big problem
  reward -= 1.0 if response.tool != ideal_response.tool
  
  # We know we needed to use the right tool
  if response.tool == ideal_response.tool
    reward += 0.5 

    if is_valid_tool_call(response.tool_call)
      reward += 0.3
    else
      # A broken tool call is bad, we don't want to learn wrong syntax
      reward -= 1.0 
    end
  end

  reward
end