Build generalist judge models for evaluating LLM outputs using verifiable rewards and policy gradient training. Create a 7B model competitive with much larger judges through reward-guided optimization and critical thinking decomposition. Use when you need reliable automated evaluation of model outputs across diverse tasks.
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Build generalist judge models for evaluating LLM outputs using verifiable rewards and policy gradient training. Create a 7B model competitive with much larger judges through reward-guided optimization and critical thinking decomposition. Use when you need reliable automated evaluation of model outputs across diverse tasks.
CompassJudger-2: Generalist Judge Models via Verifiable Rewards
Evaluating LLM outputs at scale requires automated judge models that can compare generations and rank quality. Existing approaches rely purely on supervised fine-tuning, limiting learning signal to labeled examples. CompassJudger-2 introduces verifiable rewards—binary signals indicating correctness at designated positions—combined with policy gradient optimization to train more capable judges. The 7B variant achieves competitive performance with significantly larger judge models through this hybrid training approach.
The key insight is that judge models benefit from two signals: supervision through correct answer/judgment examples, and reinforcement through explicitly-marked rewards at decision points. By decomposing judgment into five reasoning steps (demand analysis, strength/weakness identification, reasoning, prediction) and using rejection sampling for hard negatives, the model learns robust evaluation capabilities with minimal data.
Core Concept
CompassJudger-2 trains judges through a three-phase pipeline: (1) curate task-driven evaluation data with ground truth answers, (2) decompose judgment into structured reasoning steps via chain-of-thought supervision, (3) apply policy gradient optimization to reinforce correct predictions at designated positions. The verifiable reward signal is crucial: rather than comparing full outputs subjectively, rewards mark specific positions where correctness is verifiable (e.g., "is this the correct answer?").
This allows the model to learn what patterns correlate with correctness, not just memorize example judgments. The approach is agnostic to base model, requiring only supervised fine-tuning followed by policy gradient training without architectural changes.
Architecture Overview
Data Curation Pipeline: Reconstructs outdated judge data using stronger models, enhances diversity via prompt templates, synthesizes knowledge-based evaluations
Critical Thinking Framework: Decomposes judgment into five steps: user demand analysis, identification of response strengths/weaknesses, reasoning about quality, and final prediction
Verifiable Reward System: Rule-based binary signals (1 if correct at designated position, 0 otherwise) enabling clear optimization targets
Policy Gradient Optimization: Combines SFT loss with margin policy loss using rejection sampling for contrastive negatives
Rejection Sampling Module: Generates multiple candidate responses and filters for ground truth correctness as training negatives
Implementation
Data Curation and Synthesis
Build diverse evaluation datasets with verifiable ground truths.
"""
Reconstruct outdated evaluation examples using a stronger model.
Args:
old_examples: List of {'input': ..., 'answer': ...} pairs
Returns:
Reconstructed examples with modern quality standards
"""
for
in
# Use curator model to re-evaluate old examples
f"""Given this task:
{example['input']}
Provide a comprehensive evaluation. Consider:
1. Whether the previous answer was correct
2. What a better answer would be
3. Key quality metrics
Your evaluation:"""
self
'pt'
with
self
300
0.7
self
0
True
'original_input'
'input'
'original_answer'
'answer'
'modern_evaluation'
return
def
enhance_diversity_with_templates
self, base_examples: list
list
"""
Create diverse evaluation variants using prompt templates.
Args:
base_examples: Original evaluation examples
Returns:
Expanded dataset with template variations
"""
"Evaluate this response for correctness: {response}"
"Is this answer accurate? {response}"
"Rate the quality of this response: {response}"
"What is wrong with this answer? {response}"
"How would you improve this response? {response}"
for
in
for
in
'instruction'
format
'response'
''
return
def
synthesize_knowledge_evaluations
self, domains: list, num_per_domain: int = 50
list
"""
Synthesize evaluation examples with ground truth answers.
Args:
domains: List of domains (math, science, coding, etc.)
num_per_domain: Examples per domain
Returns:
Synthetic evaluation dataset with verifiable answers
"""
for
in
f"""Generate {num_per_domain} evaluation examples for the {domain} domain.
For each example, provide:
1. A question or task
2. A response to evaluate
3. Whether it's correct (yes/no)
4. Why it's correct or incorrect
Format:
Example 1:
Question: [question]
Response: [response]
Correct: [yes/no]
Reasoning: [reasoning]"""
self
'pt'
with
self
2000
0.8
self
0
True
# Parse generated examples
# (simplified: in practice use robust parsing)
'domain'
'examples'
return
class
VerifiableRewardSystem
"""Generate verifiable reward signals for judge training."""
"""
Create binary reward signal (1 if correct, 0 if wrong).
Args:
response: Model's response
ground_truth: Correct answer/judgment
task_type: Task category (qa, classification, etc.)
Returns:
Reward: 1 if correct, 0 if incorrect
"""
if
'qa'
# Exact match or semantic similarity for QA
return
1
if
else
0
elif
'classification'
# Extract predicted class and compare
1
if
else
""
return
1
if
else
0
elif
'scoring'
# Numeric scoring: reward if within threshold
try
float
float
return
1
if
abs
0.5
else
0
except
return
0
return
0
@staticmethod
def
mark_verifiable_positions
full_text: str, answer_position: int
str
"""
Mark specific positions in text where rewards are verifiable.
Args:
full_text: Complete judge response
answer_position: Character offset of final answer
Returns:
Marked text with reward positions indicated
"""
# Insert marker before answer
"[REWARD_POSITION]"
return
Critical Thinking Decomposition Framework
Structure judgment into five interpretable reasoning steps.
classCriticalThinkingFramework:
"""Decompose judgment into structured reasoning steps.""" @staticmethoddefconstruct_judgment_prompt(
task: str,
response: str) -> str:
"""
Create prompt that decomposes judgment into five steps.
Args:
task: Original task/question
response: Response to evaluate
Returns:
Structured prompt guiding judgment decomposition
"""
prompt = f"""Evaluate this response using critical thinking:
Task: {task}
Response: {response}
Please analyze in five steps:
1. USER DEMAND ANALYSIS: What is the user really asking for? What are the requirements?
[Your analysis]
2. RESPONSE STRENGTHS: What aspects of this response are good or correct?
[Your analysis]
3. RESPONSE WEAKNESSES: What aspects are lacking, incorrect, or could be improved?
[Your analysis]
4. REASONING: Weighing strengths vs. weaknesses, what is the overall quality?
[Your analysis]
5. FINAL JUDGMENT: Is this response satisfactory? Yes or No.
[Your judgment]"""return prompt
@staticmethoddefextract_final_judgment(model_output: str) -> str:
"""
Extract final judgment (Yes/No) from model output.
Args:
model_output: Full model generation with five reasoning steps
Returns:
Final judgment ("Yes" or "No")
"""
lines = model_output.split('\n')
# Find line starting with "5. FINAL JUDGMENT"for i, line inenumerate(lines):
if'5. FINAL JUDGMENT'in line or'FINAL JUDGMENT'in line:
# Next non-empty line is the judgmentfor j inrange(i + 1, len(lines)):
answer_line = lines[j].strip()
if answer_line:
# Extract yes/noif'yes'in answer_line.lower():
return'Yes'elif'no'in answer_line.lower():
return'No'return'Unknown'
Policy Gradient Optimization with Rejection Sampling
Train with both supervised loss and reinforcement signals.
import torch.nn.functional as F
from torch.optim import AdamW
classJudgeTrainer:
"""Train judge models with SFT + policy gradient."""def__init__(self, model_name: str, base_lr: float = 6e-5):
self.model = AutoModelForCausalLM.from_pretrained(model_name)
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.optimizer = AdamW(self.model.parameters(), lr=base_lr)
self.base_lr = base_lr
defrejection_sample_negatives(
self,
prompt: str,
num_candidates: int = 8,
ground_truth_label: str = 'Yes') -> list:
"""
Generate multiple responses and filter for correctness diversity.
Args:
prompt: Judge prompt
num_candidates: Number of responses to generate
ground_truth_label: Correct answer to filter for
Returns:
List of (response, is_correct) tuples
"""
input_ids = self.tokenizer.encode(prompt, return_tensors='pt')
responses = []
for _ inrange(num_candidates):
with torch.no_grad():
output_ids = self.model.generate(
input_ids,
max_new_tokens=200,
temperature=0.8,
top_p=0.95,
do_sample=True
)
response = self.tokenizer.decode(output_ids[0], skip_special_tokens=True)
judgment = CriticalThinkingFramework.extract_final_judgment(response)
is_correct = (judgment == ground_truth_label)
responses.append((response, is_correct))
return responses
defcompute_margin_loss(
self,
positive_logits: torch.Tensor,
negative_logits: torch.Tensor,
gamma: float = 10.0) -> torch.Tensor:
"""
Compute margin policy loss pushing positive away from negative.
Loss = max(0, γ - (logits_pos - logits_neg))
Args:
positive_logits: Log probabilities of correct responses
negative_logits: Log probabilities of incorrect responses
gamma: Margin parameter
Returns:
Margin loss value
"""
margin = positive_logits - negative_logits
loss = F.relu(gamma - margin)
return loss.mean()
deftraining_step(
self,
prompt: str,
ground_truth_label: str,
correct_response: str = None,
lambda_sft: float = 0.5,
lambda_rl: float = 0.5,
margin_gamma: float = 10.0) -> dict:
"""
Single training step combining SFT and policy gradient.
Args:
prompt: Judge prompt
ground_truth_label: Correct judgment
correct_response: Reference correct response (optional)
lambda_sft: Weight for SFT loss
lambda_rl: Weight for RL loss
margin_gamma: Margin parameter
Returns:
Loss dictionary with individual components
"""self.model.train()
# SFT Loss: standard language modeling on correct response
sft_loss = Noneif correct_response:
input_ids = self.tokenizer.encode(
prompt + correct_response,
return_tensors='pt'
)
labels = input_ids.clone()
# Only compute loss on response part
prompt_len = len(self.tokenizer.encode(prompt))
labels[:, :prompt_len] = -100# Ignore prompt tokens
outputs = self.model(input_ids, labels=labels)
sft_loss = outputs.loss
# Rejection Sampling: generate correct and incorrect responses
sampled = self.rejection_sample_negatives(
prompt,
num_candidates=8,
ground_truth_label=ground_truth_label
)
positive_responses = [r for r, correct in sampled if correct]
negative_responses = [r for r, correct in sampled ifnot correct]
# Policy Gradient Loss: margin between positive and negative
rl_loss = Noneif positive_responses and negative_responses:
pos_response = positive_responses[0]
neg_response = negative_responses[0]
# Compute log probabilities
pos_ids = self.tokenizer.encode(prompt + pos_response, return_tensors='pt')
neg_ids = self.tokenizer.encode(prompt + neg_response, return_tensors='pt')
with torch.no_grad():
pos_logits = self.model(pos_ids).logits
neg_logits = self.model(neg_ids).logits
# Margin loss
pos_score = pos_logits.sum() # Simplified
neg_score = neg_logits.sum()
rl_loss = self.compute_margin_loss(pos_score, neg_score, margin_gamma)
# Combined loss
total_loss = 0.0if sft_loss isnotNone:
total_loss += lambda_sft * sft_loss
if rl_loss isnotNone:
total_loss += lambda_rl * rl_loss
# Backward and optimizeself.optimizer.zero_grad()
total_loss.backward()
self.optimizer.step()
return {
'total_loss': total_loss.item(),
'sft_loss': sft_loss.item() if sft_loss elseNone,
'rl_loss': rl_loss.item() if rl_loss elseNone
}
deftrain_epoch(self, training_data: list, epochs: int = 1):
"""Train for multiple epochs."""for epoch inrange(epochs):
total_loss = 0.0for item in training_data:
loss_dict = self.training_step(
prompt=item['prompt'],
ground_truth_label=item['label'],
correct_response=item.get('correct_response')
)
total_loss += loss_dict['total_loss']
avg_loss = total_loss / len(training_data)
print(f"Epoch {epoch+1}: avg_loss={avg_loss:.4f}")
Evaluation of Judge Model
Test judge accuracy across diverse benchmarks.
classJudgeEvaluator:
"""Evaluate judge model accuracy on evaluation benchmarks."""def__init__(self, judge_model_name: str):
self.judge = AutoModelForCausalLM.from_pretrained(judge_model_name)
self.tokenizer = AutoTokenizer.from_pretrained(judge_model_name)
self.judge.eval()
defevaluate_on_benchmark(
self,
benchmark: list,
num_samples: int = None) -> dict:
"""
Evaluate judge on benchmark dataset.
Args:
benchmark: List of {'task': ..., 'response': ..., 'label': ...}
num_samples: Limit evaluation to N samples
Returns:
Accuracy and detailed results
"""if num_samples:
benchmark = benchmark[:num_samples]
correct = 0
total = len(benchmark)
for item in benchmark:
prompt = CriticalThinkingFramework.construct_judgment_prompt(
item['task'],
item['response']
)
input_ids = self.tokenizer.encode(prompt, return_tensors='pt')
with torch.no_grad():
output_ids = self.judge.generate(
input_ids,
max_new_tokens=200,
temperature=0.0
)
judgment = self.tokenizer.decode(output_ids[0], skip_special_tokens=True)
final_answer = CriticalThinkingFramework.extract_final_judgment(judgment)
is_correct = (final_answer == item['label'])
correct += int(is_correct)
accuracy = correct / total if total > 0else0.0return {
'accuracy': accuracy,
'correct': correct,
'total': total
}
Practical Guidance
Key Hyperparameters
Parameter
Value
Notes
Base Learning Rate
6e-5
Standard for fine-tuning 7B models
Batch Size
512
During SFT phase
Rejection Sampling Candidates
8
Balance diversity and computation
Margin Parameter γ
10.0
Controls positive/negative separation
SFT Loss Weight λ_sft
0.5
Balance with RL component
RL Loss Weight λ_rl
0.5
Equal weight to SFT initially
Training Epochs
1
Single pass sufficient with hard negatives
Temperature (inference)
0.0
Greedy decoding for consistency
When to Use
Building automated judges for LLM output evaluation
Comparing generations when human evaluation is infeasible
Training reward models for RLHF pipelines
Creating task-specific evaluation models without fine-tuning from scratch
Building multi-domain evaluation systems
When NOT to Use
Tasks requiring nuanced human judgment (no clear ground truth)
