Otu Anyị Si Zụọ Shannon V1.5 Ka O Chee Echiche Site na Iji GRPO

1. Ihe Kpali Anyị: Gịnị Mere Ụdị Echiche Ji Dị Mkpa?

MakaAI red teamnnyocha, ịghọtaotuotu AI si enweta nsonaazụ nwere ike ịkpata nsogbu dị mkpa dị ka ịmụ nsonaazụ ahụ n'onwe ha. Ụdị echiche Shannon V1.5 na-ekpughe usoro echiche zuru oke, na-enyere ndị nnyocha aka ka ha:

• Chọpụta ụzọ echiche na-eduga nansonaazụ AI na-enweghị nyochaomume
• Chọpụta ebe nchekwa ga-etinye aka na-emekarị (na-egosimkpa nchekwa AI)
• Mepụta usoro nchekwa siri ike karị na-elekwasị anya n'ụdị echiche akọwapụtara
• Mepụta ikpe nnwale ndị na-emegide onwe ha ka mma dabere na usoro echiche a hụrụ

2. Ịghọta GRPO

Group Relative Policy Optimization (GRPO)bụ ọganihu karịa usoro RLHF ọdịnala nke na-eme ka ọzụzụ ikike iche echiche kwụsie ike ma dị irè karị. DeepSeek AI mepụtara ya, ọ gosipụtala na ọ dị irè karịsịa maka ọzụzụ usoro echiche.

Gịnị Mere GRPO Ji Ka Mma Karịa RLHF Ọdịnala?

Ntọala Mgbakọ na Mwepụ GRPO

GRPO na-eme ka amụma dị mma site na iji tụnyere nzaghachi n'ime otu kama iji ụdị ụgwọ ọrụ zuru oke:

Ntụnyere a metụtara nwere ọtụtụ uru:

• Nkwụsi Ike:Na-edozi onwe ya maka ihe isi ike dị iche iche n'ofe ajụjụ
• Nkwụsi Ike:Na-ebelata ọdịiche dị na atụmatụ gradient
• Ịrụ Ọrụ nke Ọma:Ọ dịghị mkpa ụdị ụgwọ ọrụ dị iche

def compute_grpo_loss(
    policy_logprobs: torch.Tensor,
    rewards: torch.Tensor,
    group_size: int = 8
) -> torch.Tensor:
    """
    Compute GRPO loss with group-relative reward normalization.
    
    Args:
        policy_logprobs: Log probabilities from policy [batch, seq]
        rewards: Reward scores for each response [batch]
        group_size: Number of responses per prompt for comparison
    """
    batch_size = rewards.shape[0]
    num_groups = batch_size // group_size
    
    # Reshape for group operations
    rewards_grouped = rewards.view(num_groups, group_size)
    logprobs_grouped = policy_logprobs.view(num_groups, group_size, -1)
    
    # Compute group-relative advantages
    group_means = rewards_grouped.mean(dim=1, keepdim=True)
    group_stds = rewards_grouped.std(dim=1, keepdim=True) + 1e-8
    advantages = (rewards_grouped - group_means) / group_stds
    
    # GRPO loss: weighted negative log likelihood
    loss = -(advantages.unsqueeze(-1) * logprobs_grouped).sum(dim=-1).mean()
    
    return loss

3. Nchịkọta DeepSeek

Iji malite ikike iche echiche Shannon V1.5, anyị chịkọtara usoro echiche sitere na ụdị echiche DeepSeek. Nke a nyere usoro CoT dị elu iji zụọ isi echiche anyị.

Nchịkọta Data DeepSeek

Usoro Nchịkọta Akara

Anyị chịkọtara akara echiche n'ofe ngalaba dị iche iche iji hụ na mkpuchi echiche zuru oke:

class DeepSeekDistiller:
    """Distill chain-of-thought traces from DeepSeek models."""
    
    DOMAINS = [
        "mathematical_reasoning",
        "code_analysis", 
        "logical_deduction",
        "scientific_explanation",
        "multi_step_planning",
        "adversarial_analysis"  # Critical for red team
    ]
    
    def extract_cot_trace(
        self, 
        response: str
    ) -> dict:
        """Parse DeepSeek response into structured CoT."""
        
        # DeepSeek uses ... tags
        think_match = re.search(
            r'(.*?)', 
            response, 
            re.DOTALL
        )
        
        if not think_match:
            return None
            
        thinking = think_match.group(1)
        final_answer = response.split('')[-1].strip()
        
        # Parse individual reasoning steps
        steps = self.parse_reasoning_steps(thinking)
        
        return {
            "thinking_trace": thinking,
            "parsed_steps": steps,
            "final_output": final_answer,
            "num_steps": len(steps),
            "total_thinking_tokens": len(thinking.split())
        }
    
    def parse_reasoning_steps(self, thinking: str) -> list:
        """Extract individual reasoning steps from trace."""
        # Split on common step indicators
        step_patterns = [
            r'\n\d+\.',           # "1. ", "2. "
            r'\nStep \d+:',       # "Step 1:"
            r'\n(?:First|Next|Then|Finally),',
            r'\n- '              # Bullet points
        ]
        
        combined_pattern = '|'.join(step_patterns)
        steps = re.split(combined_pattern, thinking)
        
        return [s.strip() for s in steps if s.strip()]

4. Nhazi Isi Echiche

Ụdị Shannon V1.5 na-agụnye nke raara onwe ya nyeisi echichenke na-emepụta akara echiche doro anya tupu mmepụta ikpeazụ. Mgbakwunye nhazi a na-enyere CoT doro anya aka n'ebughị ụzọ gbanwee nhazi Mixtral bụ isi.

Mmejuputa Isi Echiche

class ThinkingHead(nn.Module):
    """
    Dedicated thinking module for Shannon V1.5.
    Generates explicit chain-of-thought traces.
    """
    
    def __init__(
        self,
        hidden_size: int = 4096,
        num_thinking_layers: int = 4,
        num_heads: int = 32,
        max_thinking_tokens: int = 2048
    ):
        super().__init__()
        
        self.hidden_size = hidden_size
        self.max_thinking_tokens = max_thinking_tokens
        
        # Special tokens
        self.think_start = nn.Parameter(torch.randn(1, 1, hidden_size))
        self.think_end = nn.Parameter(torch.randn(1, 1, hidden_size))
        
        # Thinking transformer layers
        self.thinking_layers = nn.ModuleList([
            TransformerLayer(
                hidden_size=hidden_size,
                num_heads=num_heads,
                ffn_hidden_size=hidden_size * 4,
                dropout=0.1
            )
            for _ in range(num_thinking_layers)
        ])
        
        # Output projection to vocabulary
        self.output_proj = nn.Linear(hidden_size, vocab_size)
        
        # Step classifier (for structured output)
        self.step_classifier = nn.Linear(hidden_size, 5)  # 5 step types
    
    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        generate_steps: bool = True
    ) -> dict:
        """
        Generate thinking trace from input hidden states.
        
        Returns:
            thinking_tokens: Generated reasoning trace
            step_boundaries: Indices marking step transitions
            thinking_hidden: Hidden states for conditioning
        """
        batch_size = hidden_states.shape[0]
        
        # Prepend thinking start token
        thinking_input = torch.cat([
            self.think_start.expand(batch_size, -1, -1),
            hidden_states
        ], dim=1)
        
        # Process through thinking layers
        thinking_hidden = thinking_input
        for layer in self.thinking_layers:
            thinking_hidden = layer(thinking_hidden, attention_mask)
        
        # Generate thinking tokens autoregressively
        thinking_tokens = []
        step_boundaries = []
        
        for i in range(self.max_thinking_tokens):
            logits = self.output_proj(thinking_hidden[:, -1, :])
            next_token = logits.argmax(dim=-1)
            
            # Check for step boundaries
            step_type = self.step_classifier(thinking_hidden[:, -1, :])
            if step_type.argmax(dim=-1) != 0:  # 0 = continue
                step_boundaries.append(i)
            
            thinking_tokens.append(next_token)
            
            # Check for think_end
            if next_token == self.think_end_token_id:
                break
            
            # Update for next iteration
            # ... (autoregressive generation logic)
        
        return {
            "thinking_tokens": torch.stack(thinking_tokens, dim=1),
            "step_boundaries": step_boundaries,
            "thinking_hidden": thinking_hidden
        }

5. Usoro Ọzụzụ

Nzọụkwụ 1: Ọzụzụ Mbụ Isi Echiche

Nke mbụ, anyị na-azụ isi echiche na akara CoT DeepSeek-distilled site na iji mfu cross-entropy ọkọlọtọ:

# Thinking Head Pre-training Configuration
model:
  base: shannon-ai/v1-deep  # Start from GPT-5 distilled model
  thinking_head:
    num_layers: 4
    hidden_size: 4096
    max_tokens: 2048

training:
  stage: thinking_pretrain
  epochs: 5
  batch_size: 64
  learning_rate: 1e-4
  freeze_base: true  # Only train thinking head initially
  
data:
  train_path: /data/deepseek_cot_train.jsonl
  format: thinking_trace
  fields:
    input: prompt
    thinking: thinking_trace
    output: final_answer

Nzọụkwụ 2: Ndozi GRPO

Mgbe ọzụzụ mbụ gasịrị, anyị na-etinye GRPO iji meziwanye ogo echiche site na iji ntụnyere metụtara otu:

class GRPOTrainer:
    """GRPO trainer for thinking model optimization."""
    
    def __init__(
        self,
        model: ThinkingModel,
        group_size: int = 8,
        kl_coef: float = 0.1
    ):
        self.model = model
        self.group_size = group_size
        self.kl_coef = kl_coef
        self.ref_model = copy.deepcopy(model)
        self.ref_model.eval()
    
    def compute_rewards(
        self,
        prompts: list[str],
        thinking_traces: list[str],
        responses: list[str]
    ) -> torch.Tensor:
        """
        Compute rewards for thinking quality.
        Multiple signals combined for comprehensive evaluation.
        """
        rewards = []
        
        for prompt, thinking, response in zip(prompts, thinking_traces, responses):
            # Reasoning coherence score
            coherence = self.evaluate_coherence(thinking)
            
            # Step structure quality
            structure = self.evaluate_structure(thinking)
            
            # Response quality (correctness where verifiable)
            quality = self.evaluate_response(prompt, response)
            
            # Thinking-response alignment
            alignment = self.evaluate_alignment(thinking, response)
            
            # Combined reward
            reward = (
                0.3 * coherence +
                0.2 * structure +
                0.3 * quality +
                0.2 * alignment
            )
            rewards.append(reward)
        
        return torch.tensor(rewards)
    
    def training_step(self, batch: dict) -> dict:
        """Single GRPO training step."""
        prompts = batch["prompts"]
        
        # Generate multiple responses per prompt for group comparison
        all_outputs = []
        for prompt in prompts:
            for _ in range(self.group_size):
                output = self.model.generate_with_thinking(
                    prompt,
                    temperature=0.8,  # Diversity for comparison
                    do_sample=True
                )
                all_outputs.append(output)
        
        # Compute rewards
        rewards = self.compute_rewards(
            prompts=[p for p in prompts for _ in range(self.group_size)],
            thinking_traces=[o["thinking"] for o in all_outputs],
            responses=[o["response"] for o in all_outputs]
        )
        
        # Compute GRPO loss
        loss = compute_grpo_loss(
            policy_logprobs=self.get_logprobs(all_outputs),
            rewards=rewards,
            group_size=self.group_size
        )
        
        # Add KL penalty against reference model
        kl_div = self.compute_kl_divergence(all_outputs)
        total_loss = loss + self.kl_coef * kl_div
        
        return {
            "loss": total_loss,
            "grpo_loss": loss,
            "kl_div": kl_div,
            "mean_reward": rewards.mean()
        }

Nzọụkwụ 3: Ọkachamara Otu Uhie

N'ikpeazụ, anyị na-edozi ọzọ na ọnọdụ ndị na-emegide iji hụ na akara echiche na-ekpughe echiche nke ọma makaAI na-enweghị nyocha n'ihi yanyocha:

6. Nsonaazụ & Nyocha

Metrics Ogo Echiche

Nchọpụta Dị Mkpa

• Ịdị mma mụbara nke ukwuu:Site na 12% ruo 97.8% nke echiche ugbu a ka ekwupụtara n'ụzọ doro anya
• Eziokwu echiche mụbara:Echiche doro anya meziwanyere ogo azịza ikpeazụ site na ihe karịrị 12 isi
• Ekwenyere uru otu uhie:Ndị nyocha nchekwa na-akọ na akara echiche bụ "ihe bara uru" maka ịghọta echiche nrigbu
• GRPO karịrị RLHF:15% akara njikọ ka mma ma e jiri ya tụnyere usoro ọdịnala