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Train vision-language models for complex reasoning by alternating SFT (supervised fine-tuning via text-only reasoning models) and curriculum RL (Group Relative Policy Optimization). Progressively improve through iterative cycles where each iteration generates better training data.
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Convert arXiv papers into ready-to-use agent skills using category-aware extraction. First classifies the paper into one or more of 11 research categories, then applies a specialized extraction pipeline for each category — because different types of papers produce different types of usable knowledge. A single paper can yield multiple skills if it spans categories. Use this skill whenever the user wants to turn a paper into a skill, extract practical techniques from research, build a skill library from papers, convert arXiv papers into reusable agent instructions, or batch-process multiple papers into skills. Also trigger when someone asks about extracting actionable knowledge from papers, making research practical for LLM agents, or systematically converting academic contributions into structured agent capabilities.
Establish regret bounds for behavior cloning with discretized actions combining statistical error and quantization error terms. Prove smoothness requirements for safe quantizer design, show that learning-based quantizers fail these requirements, and propose model-based augmentation to reduce error dependence from H² to H.
Dynamically allocate LoRA ranks per-layer during fine-tuning instead of using fixed uniform ranks. Learn optimal rank for each layer and subject via variational framework with discretized exponential distribution, reducing memory footprint while maintaining fidelity and text-alignment.
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| name | openvlthinker-vision-reasoning |
| title | OpenVLThinker: An Early Exploration to Complex Vision-Language Reasoning via Iterative Self-Improvement |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2503.17352 |
| keywords | ["Vision-Language","Reasoning","SFT","RL","Self-Improvement","Chain-of-Thought"] |
| description | Train vision-language models for complex reasoning by alternating SFT (supervised fine-tuning via text-only reasoning models) and curriculum RL (Group Relative Policy Optimization). Progressively improve through iterative cycles where each iteration generates better training data. |
Vision-language models struggle with complex reasoning tasks because distilling reasoning from text-only models like DeepSeek-R1 often degrades visual understanding. OpenVLThinker solves this through iterative SFT-RL cycles: alternate between lightweight SFT (using text models to generate reasoning demonstrations) and curriculum RL (applying Group Relative Policy Optimization with difficulty-based scheduling). Each cycle improves training data quality, enabling self-improvement with minimal total examples.
Use a capable text-only model to generate chain-of-thought demonstrations from image captions and questions.
import torch
from typing import List, Dict
class ReasoningDataGeneration:
"""
Generate chain-of-thought reasoning using a text-only model
(e.g., QwQ-32B) from image captions and task questions.
"""
def __init__(self, text_model, device='cuda'):
self.text_model = text_model
self.device = device
def generate_reasoning(
self,
image_caption: str,
question: str,
max_length: int = 512
) -> str:
prompt =
inputs = .text_model.tokenize(prompt)
torch.no_grad():
outputs = .text_model.generate(
inputs,
max_length=max_length,
temperature=,
top_p=
)
reasoning = .text_model.decode(outputs[])
reasoning
() -> []:
sft_examples = []
caption, question, answer (captions, questions, answers):
reasoning = .generate_reasoning(caption, question)
example = {
: caption,
: question,
: reasoning,
: answer,
:
}
sft_examples.append(example)
sft_examples
Analyze whether SFT successfully induces reasoning behaviors by examining keyword frequencies in model outputs.
import re
from collections import Counter
def analyze_reasoning_keywords(model_outputs: List[str]) -> Dict[str, float]:
"""
Count reasoning-related keywords to verify SFT effectiveness.
Keywords indicate reflection, step-by-step thinking, and uncertainty.
"""
reasoning_keywords = [
'let me', 'think', 'step', 'first', 'next', 'then',
'because', 'thus', 'therefore', 'however', 'although',
'observe', 'notice', 'identify', 'reason', 'conclusion'
]
keyword_counts = Counter()
for output in model_outputs:
output_lower = output.lower()
for keyword in reasoning_keywords:
count = len(re.findall(r'\b' + keyword + r'\b', output_lower))
keyword_counts[keyword] += count
total_keywords = sum(keyword_counts.values())
keyword_frequencies = {
k: v / max(total_keywords, 1) for k, v in keyword_counts.items()
}
return keyword_frequencies
def sft_efficacy_score(
base_model_outputs: List[str],
sft_model_outputs: List[str]
) -> float:
"""
Measure SFT effectiveness by comparing keyword distribution.
Higher score indicates SFT successfully induced reasoning.
"""
base_keywords = analyze_reasoning_keywords(base_model_outputs)
sft_keywords = analyze_reasoning_keywords(sft_model_outputs)
# Compute keyword improvement ratio
improvements = [
sft_keywords.get(k, 0) / max(base_keywords.get(k, 0), 1e-6)
for k in base_keywords.keys()
]
avg_improvement = sum(improvements) / len(improvements) if improvements else 1.0
return min(avg_improvement, 2.0) # Cap at 2x improvement
Apply Group Relative Policy Optimization with progressive difficulty scheduling.
import torch.nn.functional as F
class CurriculumGRPO:
"""
Group Relative Policy Optimization with curriculum learning.
Progressively train on harder examples: medium → hard → hardest.
"""
def __init__(self, model, reference_model, device='cuda'):
self.model = model
self.reference_model = reference_model
self.device = device
def compute_difficulty_score(
self,
base_model_accuracy: float,
gpt4_rating: float
) -> float:
"""
Composite difficulty score combining two signals:
- Base model error rate (1 - accuracy)
- GPT-4 quality rating (normalized to [0,1])
"""
error_rate = 1.0 - base_model_accuracy
difficulty = (error_rate + (1 - gpt4_rating)) / 2
return difficulty
def group_by_difficulty(
self,
examples: List[Dict],
base_model_accuracy: List[float],
gpt4_ratings: List[float]
) -> Dict[str, List[Dict]]:
"""
Partition examples into difficulty tiers.
Returns: {'easy': [...], 'medium': [...], 'hard': [...]}
"""
difficulties = [
self.compute_difficulty_score(acc, rating)
for acc, rating in zip(base_model_accuracy, gpt4_ratings)
]
# Sort by difficulty and partition into thirds
sorted_indices = sorted(range(len(difficulties)), key=lambda i: difficulties[i])
n = len(sorted_indices)
easy_idx = sorted_indices[:n // 3]
medium_idx = sorted_indices[n // 3:2 * n // 3]
hard_idx = sorted_indices[2 * n // 3:]
return {
'easy': [examples[i] for i in easy_idx],
'medium': [examples[i] for i in medium_idx],
'hard': [examples[i] for i in hard_idx]
}
def grpo_loss(
self,
log_probs: torch.Tensor,
ref_log_probs: torch.Tensor,
rewards: torch.Tensor,
beta: float = 0.5
) -> torch.Tensor:
"""
GRPO loss: scales log probability ratios by reward advantage.
log_probs: current model log probabilities (batch,)
ref_log_probs: reference model log probabilities (batch,)
rewards: reward signal (batch,) - higher is better
"""
log_ratio = log_probs - ref_log_probs
# Normalize rewards for stability
rewards = (rewards - rewards.mean()) / (rewards.std() + 1e-8)
# GRPO: maximize log(pi/pi_ref) * advantage
advantage = rewards
loss = -torch.mean(log_ratio * advantage)
# KL regularization to prevent divergence from reference
kl_div = (log_probs - ref_log_probs).mean()
total_loss = loss + beta * kl_div
return total_loss
def train_curriculum_rl(
self,
training_examples: List[Dict],
base_accuracies: List[float],
gpt4_ratings: List[float],
num_epochs: int = 3,
batch_size: int = 8
):
"""
Train with curriculum: easy → medium → hard.
Each stage trains for one epoch on increasingly difficult examples.
"""
grouped = self.group_by_difficulty(
training_examples, base_accuracies, gpt4_ratings
)
curriculum_order = ['medium', 'hard'] # Start with medium, skip easy
for stage_idx, difficulty in enumerate(curriculum_order):
print(f"Training on {difficulty} examples (stage {stage_idx + 1})")
examples = grouped[difficulty]
for epoch in range(num_epochs):
total_loss = 0
num_batches = 0
for i in range(0, len(examples), batch_size):
batch = examples[i:i + batch_size]
# Prepare batch
prompts = [ex['instruction'] for ex in batch]
responses = [ex['full_response'] for ex in batch]
rewards = torch.tensor(
[ex.get('reward', 1.0) for ex in batch],
dtype=torch.float32,
device=self.device
)
# Forward pass
log_probs = self._compute_log_probs(prompts, responses)
with torch.no_grad():
ref_log_probs = self._compute_log_probs_ref(prompts, responses)
# Compute loss
loss = self.grpo_loss(log_probs, ref_log_probs, rewards)
# Backward pass
self.model.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)
self.model.optimizer.step()
total_loss += loss.item()
num_batches += 1
avg_loss = total_loss / max(num_batches, 1)
print(f" Epoch {epoch + 1} - Loss: {avg_loss:.4f}")
def _compute_log_probs(self, prompts: List[str], responses: List[str]) -> torch.Tensor:
"""Compute log probabilities from current model."""
# Placeholder: actual implementation tokenizes and computes log probs
return torch.randn(len(prompts), device=self.device)
def _compute_log_probs_ref(self, prompts: List[str], responses: List[str]) -> torch.Tensor:
"""Compute log probabilities from reference model."""
with torch.no_grad():
return torch.randn(len(prompts), device=self.device)
Orchestrate multiple SFT-RL cycles where each iteration improves training data and model quality.
class IterativeSelfImprovement:
"""
Manage iterative SFT-RL cycles: cycle 1, 2, 3.
Each cycle generates better training data from improved model.
"""
def __init__(self, vlm_model, text_model, num_cycles: int = 3):
self.vlm_model = vlm_model
self.text_model = text_model
self.num_cycles = num_cycles
self.cycle_metrics = []
def run_full_pipeline(
self,
initial_examples: List[Dict],
validation_set: List[Dict]
):
"""
Execute full iterative pipeline: SFT → RL → evaluate → repeat.
initial_examples: Starting training data (images + captions + questions)
validation_set: Hold-out validation benchmarks
"""
training_data = initial_examples
for cycle in range(self.num_cycles):
print(f"\n===== Cycle {cycle + 1} / {self.num_cycles} =====")
# Phase 1: SFT
print(f"Phase 1: Supervised Fine-Tuning on {len(training_data)} examples")
self.run_sft(training_data)
# Evaluate after SFT
sft_metrics = self.evaluate(validation_set)
print(f" SFT Metrics: {sft_metrics}")
# Phase 2: RL
print(f"Phase 2: Curriculum RL")
self.run_curriculum_rl(training_data)
# Evaluate after RL
rl_metrics = self.evaluate(validation_set)
print(f" RL Metrics: {rl_metrics}")
# Record cycle metrics
self.cycle_metrics.append({
'cycle': cycle + 1,
'sft_metrics': sft_metrics,
'rl_metrics': rl_metrics
})
# Generate new training data for next cycle (if not last)
if cycle < self.num_cycles - 1:
print(f"Phase 3: Generating training data for cycle {cycle + 2}")
training_data = self.generate_next_cycle_data(
training_data, rl_metrics
)
def run_sft(self, training_data: List[Dict]):
"""Supervised fine-tuning phase."""
# Tokenize and train standard language model loss
pass
def run_curriculum_rl(self, training_data: List[Dict]):
"""Curriculum RL phase."""
grpo = CurriculumGRPO(self.vlm_model, self.vlm_model)
# Implement curriculum training
pass
def evaluate(self, validation_set: List[Dict]) -> Dict:
"""Evaluate on validation benchmarks."""
accuracies = []
for example in validation_set:
# Generate answer
output = self.vlm_model.generate(
image=example['image'],
prompt=example['question']
)
# Compare with ground truth
correct = self._match_answer(output, example['answer'])
accuracies.append(float(correct))
return {
'accuracy': sum(accuracies) / len(accuracies) if accuracies else 0.0,
'count': len(validation_set)
}
def generate_next_cycle_data(
self,
current_data: List[Dict],
metrics: Dict
) -> List[Dict]:
"""
Generate training data for next cycle using improved model.
Reuse successful examples and add new harder examples.
"""
# Keep ~70% of current data, generate ~30% new data
num_new = len(current_data) // 3
new_data = current_data[:2 * len(current_data) // 3]
# Generate new harder examples
data_gen = ReasoningDataGeneration(self.text_model)
new_examples = data_gen.create_sft_examples(
captions=[ex['caption'] for ex in current_data[-num_new:]],
questions=[ex['question'] for ex in current_data[-num_new:]],
answers=[ex['answer'] for ex in current_data[-num_new:]]
)
new_data.extend(new_examples)
return new_data
def _match_answer(self, output: str, ground_truth: str) -> bool:
"""Simple string matching for answer evaluation."""
return output.strip().lower() == ground_truth.strip().lower()
Combine trained components for efficient inference on reasoning tasks.
class OpenVLThinkerInference:
"""
Production inference pipeline combining trained VLM with optional
reasoning generation for transparent answer production.
"""
def __init__(self, vlm_model, generate_reasoning: bool = True):
self.vlm_model = vlm_model
self.generate_reasoning = generate_reasoning
def answer_question(
self,
image: torch.Tensor,
question: str,
return_reasoning: bool = False
) -> Dict:
"""
Answer visual question with optional reasoning.
image: PIL Image or tensor (batch, 3, H, W)
question: Text question about image
"""
if self.generate_reasoning:
# Generate reasoning-based answer
prompt = f"Question: {question}\n\nLet me think through this step by step:"
else:
prompt = f"Question: {question}\nAnswer:"
# Prepare input
inputs = self.vlm_model.process(image, prompt)
# Generate response
with torch.no_grad():
outputs = self.vlm_model.generate(
**inputs,
max_length=256,
temperature=0.7,
top_p=0.9
)
response = self.vlm_model.decode(outputs[0])
# Parse answer from response
if self.generate_reasoning:
parts = response.split('Answer:')
reasoning = parts[0] if len(parts) > 0 else ""
answer = parts[1].strip() if len(parts) > 1 else response.strip()
else:
reasoning = ""
answer = response.strip()
result = {
'answer': answer,
'full_response': response
}
if return_reasoning:
result['reasoning'] = reasoning
return result
When to Use:
When NOT to Use:
Hyperparameter Tuning:
Common Pitfalls: