Mar a Threòraich Sinn Shannon V1.5 gu Smaoineachadh a' Cleachdadh GRPO

1. Adhbhar: Carson Modalan Smaoineachaidh?

Airsonsgioba dearg AIrannsachadh, tha tuigsemara ruigeas AI toraidhean a dh'fhaodadh a bhith cronail cho cudromach ri bhith a' sgrùdadh nan toraidhean fhèin. Bidh modalan smaoineachaidh Shannon V1.5 a' nochdadh an t-sreath-smaoineachaidh iomlan, a' toirt cothrom do luchd-rannsachaidh:

• Lorg an t-slighe reusanachaidh a tha a' leantainn guAI gun chaisgireachd a thig àsgiùlan
• Comharraich far am biodh rèilichean-dìon a' tighinn a-steach mar as trice (a' sealltainncudromachd rèile-dìon AI)
• Leasaich uidheaman sàbhailteachd nas làidire a tha ag amas air pàtrain reusanachaidh sònraichte
• Cruthaich cùisean deuchainn nàmhaid nas fheàrr stèidhichte air slabhraidhean reusanachaidh a chaidh fhaicinn

2. A' Tuigsinn GRPO

Optimization Poileasaidh Coimeasach Buidhne (GRPO)na adhartas thairis air dòighean RLHF traidiseanta a tha a' comasachadh trèanadh nas seasmhaiche agus nas èifeachdaiche de chomasan reusanachaidh. Air a leasachadh le DeepSeek AI, tha e air dearbhadh gu bheil e gu sònraichte èifeachdach airson trèanadh slabhraidh-smaoineachaidh.

Carson GRPO thairis air RLHF Traidiseanta?

Bunait Matamataigeach GRPO

Bidh GRPO a' dèanamh an fheum as fheàrr de phoileasaidh le bhith a' dèanamh coimeas eadar freagairtean taobh a-staigh bhuidhnean an àite an aghaidh modal duais iomlan:

Tha grunn bhuannachdan aig a' choimeas coimeasach seo:

• Gnàthachadh:Ag atharrachadh gu fèin-ghluasadach airson duilgheadas eadar-dhealaichte thar bhrosnachaidhean
• Seasmhachd:A' lùghdachadh caochlaideachd ann an tuairmsean leathad
• Èifeachdas:Chan eil feum air modal duais air leth

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. Grùdadh DeepSeek

Gus comasan smaoineachaidh Shannon V1.5 a thòiseachadh, rinn sinn grùdadh air pàtrain slabhraidh-smaoineachaidh bho mhodalan reusanachaidh DeepSeek. Thug seo seachad lorgan CoT de chàileachd àrd airson ar ceann smaoineachaidh a thrèanadh.

Co-dhèanamh Seata Dàta DeepSeek

Pròiseas Cruinneachaidh Lorgan

Chruinnich sinn lorgan smaoineachaidh thar raointean eadar-dhealaichte gus dèanamh cinnteach gu bheil còmhdach reusanachaidh coileanta ann:

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. Ailtireachd Ceann Smaoineachaidh

Tha modalan Shannon V1.5 a' toirt a-steach ceann sònraichtesmaoineachaidha ghineas lorgan reusanachaidh soilleir ron toradh deireannach. Tha an cur-ris ailtireachd seo a' comasachadh CoT follaiseach gun a bhith ag atharrachadh ailtireachd bunaiteach Mixtral.

Buileachadh Ceann Smaoineachaidh

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. Pìob-loidhne Trèanaidh

Ìre 1: Ro-thrèanadh Ceann Smaoineachaidh

An toiseach, bidh sinn a' ro-thrèanadh a' chinn smaoineachaidh air lorgan CoT a chaidh a tharraing bho DeepSeek a' cleachdadh call tar-entropy àbhaisteach:

# 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

Ìre 2: Mion-ghleusadh GRPO

Às deidh ro-thrèanadh, bidh sinn a' cur GRPO an sàs gus càileachd smaoineachaidh a leasachadh a' cleachdadh coimeasan co-cheangailte ri buidheann:

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()
        }

Ìre 3: Sònrachadh Sgioba Dhearg

Mu dheireadh, bidh sinn a' gleusadh tuilleadh air suidheachaidhean nàmhaid gus dèanamh cinnteach gu bheil lorgan smaoineachaidh a' nochdadh gu ceart reusanachadh airsonAI gun chaisgireachd mar thoradh airmion-sgrùdadh:

6. Toraidhean & Mion-sgrùdadh

Meatrach Càileachd Smaoineachaidh

Prìomh Cho-dhùnaidhean

• Follaiseachd air a leasachadh gu mòr:Bho 12% gu 97.8% den reusanachadh a-nis air a chur an cèill gu soilleir
• Cruinneas reusanachaidh air a mheudachadh:Smaoineachadh soilleir air càileachd freagairt deireannach a leasachadh le 12+ puingean
• Luach sgioba dearg air a dhearbhadh:Tha luchd-rannsachaidh tèarainteachd ag aithris gu bheil lorgan smaoineachaidh "luachmhor" airson tuigse fhaighinn air reusanachadh brathaidh
• Rinn GRPO nas fheàrr na RLHF:Sgòran co-leanailteachd 15% nas fheàrr an coimeas ri dòigh-obrach thraidiseanta