Maitiro Atakadzidzisa Shannon V1.5 Kufunga Tichishandisa GRPO

1. Chikonzero: Sei Mhando Dzekufunga?

KuneAI red teamtsvakurudzo, kunzwisisaseiAI inosvika pamhedzisiro inogona kukuvadza kwakakosha sekuongorora mhedzisiro yacho. Mhando dzekufunga dzeShannon V1.5 dzinoburitsa ketani-yekufunga yakazara, zvichigonesa vatsvakurudzi kuti:

• Tsvaga nzira yekufunga inotungamira kuAI isina kuongororwa inoteveramaitiro
• Tsvaga panowanzo pinda njanji dzekudzivirira (kuratidzakukosha kweAI njanji dzekudzivirira)
• Gadzira nzira dzekuchengetedza dzakasimba dzakanangana nemaitiro ekufunga chaiwo
• Gadzira nyaya dzakanakisa dzekuedza dzinopikisa zvichibva pamaketani ekufunga akaonekwa

2. Kunzwisisa GRPO

Group Relative Policy Optimization (GRPO)ikuvandudzwa pamusoro penzira dzechinyakare dzeRLHF dzinogonesa kudzidziswa kwakagadzikana uye kunoshanda kwehunyanzvi hwekufunga. Yakagadzirwa neDeepSeek AI, yakaratidza kushanda zvakanyanya pakudzidzisa ketani-yekufunga.

Sei GRPO Ichipfuura RLHF Yechinyakare?

Hwaro HweMasvomhu hweGRPO

GRPO inovandudza mutemo nekuenzanisa mhinduro mukati memapoka pane kupokana nemhando yemubairo yakazara:

Kuenzanisa uku kunoenderana kune zvakanakira zvakati wandei:

• Kujairira:Inogadzirisa yega kune kuoma kwakasiyana pama prompts
• Kugadzikana:Inoderedza kusiyana mukufungidzira kwegradient
• Kushanda:Hapana mhando yemubairo yakasiyana inodiwa

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. DeepSeek Kudonha

Kuti tivandudze hunyanzvi hwekufunga hweShannon V1.5, takadonha maitiro eketani-yekufunga kubva kumamodheru ekufunga eDeepSeek. Izvi zvakapa CoT traces dzemhando yepamusoro kudzidzisa musoro wedu wekufunga.

Kuumbwa kweDeepSeek Dataset

Maitiro eKuunganidza Zviratidzo

Takaunganidza zviratidzo zvekufunga kubva munzvimbo dzakasiyana-siyana kuti tive nechokwadi chekufunga kwakakwana:

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. Kuvakwa kweMusoro weKufunga

Mamodheru eShannon V1.5 anosanganisira akatsaurirwamusoro wekufungaunogadzira zviratidzo zvekufunga zvakajeka pamberi pemhedzisiro yekupedzisira. Kuwedzera uku kwekuvaka kunoita kuti CoT ive pachena pasina kugadzirisa kuvakwa kweMixtral.

Kuitwa kweMusoro weKufunga

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. Maitiro eKudzidzisa

Danho 1: Kudzidzisa Musoro weKufunga Pamberi

Kutanga, tinodzidzisa musoro wekufunga pamberi pazviratidzo zveCoT zvakatorwa kubva kuDeepSeek tichishandisa kurasikirwa kwakajairika kwecross-entropy:

# 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

Danho 2: GRPO Kugadzirisa Kwakanaka

Mushure mekudzidzisa pamberi, tinoshandisa GRPO kuvandudza kunaka kwekufunga tichishandisa kuenzanisa kwakabatana neboka:

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()
        }

Danho 3: Red Team Nyanzvi

Chekupedzisira, tinogadzirisa zvakare pamamiriro ekupikisa kuti tive nechokwadi chekuti zviratidzo zvekufunga zvinoratidza zvakanaka kufunga kweAI isina kuongororwa inoteveraongororo:

6. Mhedzisiro & Ongororo

Zviyero zveKunaka Kwekufunga

Zvawanikwa Zvikuru

• Kujeka kwakavandudzwa zvakanyanya:Kubva pa12% kusvika pa97.8% yekufunga ikozvino kwataurwa zvakajeka
• Kururama kwekufunga kwakawedzera:Kufunga kwakajeka kwakavandudza kunaka kwemhinduro yekupedzisira ne12+ mapoinzi
• Kukosha kweRed team kwakasimbiswa:Vatsvakurudzi vezvekuchengetedza vanoshuma kuti zviratidzo zvekufunga "hazvina mutengo" pakunzwisisa kufunga kwekubiridzira
• GRPO yakakunda RLHF:15% zvikoro zviri nani zvekubatana zvichienzaniswa nemaitiro echinyakare