菜单
Adapt large reasoning models for optimization tasks using expert-guided hint correction. Generate high-quality training data with minimal expert intervention (<2.6% token modification). Trigger: fine-tune reasoning models on domain-specific tasks without large supervised datasets.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/ADu2021/skillXiv --skill calm-reasoning命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
基于 SOC 职业分类
| name | calm-reasoning |
| title | CALM Before STORM: Corrective Adaptation of Reasoning Models with Expert Hints |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2510.04204 |
| keywords | ["reasoning-models","domain-adaptation","expert-guidance","data-synthesis","efficient-adaptation"] |
| description | Adapt large reasoning models for optimization tasks using expert-guided hint correction. Generate high-quality training data with minimal expert intervention (<2.6% token modification). Trigger: fine-tune reasoning models on domain-specific tasks without large supervised datasets. |
Large Reasoning Models (LRMs) possess advanced thinking capabilities but often fail on domain-specific tasks like optimization modeling. CALM (Corrective Adaptation with Lightweight Modification) enables efficient adaptation by leveraging an expert to identify reasoning flaws and provide corrective hints. The model then uses these hints to generate improved reasoning trajectories—requiring modification of fewer than 2.6% of tokens. This creates a high-quality synthetic dataset for fine-tuning that preserves the LRM's native reasoning patterns.
The key insight: An expert doesn't need to write complete solutions; pointing out flaws lets the LRM fix its own reasoning, preserving generalization.
Expert review identifies where the baseline LRM's reasoning breaks down.
class ReasoningFlawDetector:
"""
Identify reasoning errors in LRM output.
Expert marks flaws with corrective hints.
"""
def __init__(self, lrm_model, domain_validator):
self.model = lrm_model
self.validator = domain_validator # Domain-specific correctness check
def detect_flaws_in_trace(self, problem, reasoning_trace):
"""
Analyze reasoning trace for domain-specific errors.
Args:
problem: Problem statement
reasoning_trace: Model's reasoning text
Returns:
List of (position, error_type, expert_hint)
"""
flaws = []
# Parse reasoning into logical steps
steps = parsing_model.extract_steps(reasoning_trace)
step_idx, step (steps):
step_validation = .validator.validate_step(
problem,
step,
prior_steps=steps[:step_idx]
)
step_validation[]:
flaw = {
: step_idx,
: step_validation[],
: step,
:
}
flaws.append(flaw)
flaws
():
()
()
()
flaw_id, flaw (detected_flaws):
()
()
hint = ().strip()
flaw[] = hint
detected_flaws
Given hints, have the LRM generate improved reasoning traces.
class SyntheticDataSynthesizer:
"""Generate high-quality reasoning traces using expert hints."""
def __init__(self, lrm_model):
self.model = lrm_model
def synthesize_improved_trace(self, problem, original_trace, annotated_flaws):
"""
Generate improved reasoning by providing hints at flaw locations.
Args:
problem: Problem statement
original_trace: Model's initial reasoning
annotated_flaws: List of flaws with expert hints
Returns:
Improved reasoning trace
"""
# Build prompt with problem and hints at flaw locations
prompt = f"Problem: {problem}\n\nReasoning with corrections:\n"
# Rewrite trace with hints integrated
steps = parsing_model.extract_steps(original_trace)
for step_idx, step in enumerate(steps):
# Check if this step has a flaw with hint
flaw_hint = None
for flaw in annotated_flaws:
if flaw["position"] == step_idx:
flaw_hint = flaw["expert_hint"]
break
if flaw_hint:
# Provide hint and ask model to regenerate this step
prompt += f"Step {step_idx}: {step}\n"
prompt += f" Correction hint: {flaw_hint}\n"
prompt += f" Improved step: "
# Generate improved version
improved = self.model.generate(
prompt,
max_tokens=100,
temperature=0.3
)
prompt += f"{improved}\n"
else:
# Step was correct
prompt += f"Step {step_idx}: {step}\n"
# Generate final improved reasoning
final_trace = self.model.generate(
prompt + "\n[Complete reasoning]:\n",
max_tokens=1500,
temperature=0.2
)
return final_trace
def compute_token_modification_ratio(self, original, improved):
"""
Calculate what fraction of tokens were changed.
"""
orig_tokens = original.split()
improved_tokens = improved.split()
# Simple token-level edit distance
from difflib import SequenceMatcher
matcher = SequenceMatcher(None, orig_tokens, improved_tokens)
matching_ratio = matcher.ratio()
modification_ratio = 1.0 - matching_ratio
return modification_ratio
Combine original problem, expert hints, and synthetic improved traces into training data.
class SyntheticDataset:
"""Manages synthetic training data generation and storage."""
def __init__(self, synthesizer, validator):
self.synthesizer = synthesizer
self.validator = validator
self.data = []
self.token_modification_stats = []
def build_dataset_with_hints(self, problems, original_traces, num_examples=None):
"""
Generate synthetic dataset for all examples.
Args:
problems: List of problem statements
original_traces: Corresponding LRM reasoning traces
num_examples: Limit to subset (for expert annotation)
Returns:
List of training examples
"""
if num_examples is None:
num_examples = len(problems)
for idx in range(min(num_examples, len(problems))):
problem = problems[idx]
original_trace = original_traces[idx]
# Detect flaws (automatic) and annotate (expert)
detector = ReasoningFlawDetector(self.model, self.validator)
detected_flaws = detector.detect_flaws_in_trace(
problem,
original_trace
)
if len(detected_flaws) == 0:
# No flaws; use original trace
improved_trace = original_trace
else:
# Expert annotates flaws
annotated_flaws = detector.expert_annotation_interface(
problem,
original_trace,
detected_flaws
)
# Synthesize improved trace
improved_trace = self.synthesizer.synthesize_improved_trace(
problem,
original_trace,
annotated_flaws
)
# Measure token modification
mod_ratio = self.synthesizer.compute_token_modification_ratio(
original_trace,
improved_trace
)
self.token_modification_stats.append(mod_ratio)
# Add to dataset
example = {
"problem": problem,
"reasoning": improved_trace,
"flaws_corrected": len(detected_flaws),
"token_modification_ratio": mod_ratio
}
self.data.append(example)
print(f"Example {idx}: {len(detected_flaws)} flaws, "
f"{mod_ratio*100:.1f}% tokens modified")
return self.data
def statistics(self):
"""Report dataset statistics."""
print(f"\nDataset Statistics:")
print(f" Examples: {len(self.data)}")
print(f" Avg token modification: {np.mean(self.token_modification_stats)*100:.2f}%")
print(f" Max token modification: {np.max(self.token_modification_stats)*100:.2f}%")
Train the adapted model using both supervised learning and reinforcement learning.
def finetune_lrm_with_synthetic_data(
base_model,
synthetic_dataset,
validator,
config
):
"""
Two-phase fine-tuning: SFT then RL.
"""
optimizer = torch.optim.Adam(base_model.parameters(), lr=2e-5)
# Phase 1: Supervised Fine-Tuning on synthetic data
print("Phase 1: Supervised Fine-Tuning")
for epoch in range(config.sft_epochs):
for example in synthetic_dataset.data:
problem = example["problem"]
improved_reasoning = example["reasoning"]
# Generate and compute loss
output = base_model.generate(
problem,
return_logits=True
)
# Token-level cross-entropy loss
loss = compute_cross_entropy_loss(
output["logits"],
improved_reasoning
)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Phase 2: Reinforcement Learning for correctness
print("\nPhase 2: Reinforcement Learning Refinement")
for epoch in range(config.rl_epochs):
for example in synthetic_dataset.data:
problem = example["problem"]
ground_truth = example.get("ground_truth")
# Generate reasoning
reasoning = base_model.generate(problem, max_tokens=1500)
# Validate correctness
validation = validator.validate_solution(
problem,
reasoning
)
reward = 1.0 if validation["is_correct"] else 0.0
# Policy gradient loss
log_prob = base_model.compute_log_prob(reasoning)
loss = -reward * log_prob
optimizer.zero_grad()
loss.backward()
optimizer.step()
return base_model
Assess performance on downstream tasks (STORM datasets: optimization modeling benchmarks).
def evaluate_adapted_lrm(adapted_model, benchmark_datasets):
"""
Evaluate CALM-adapted model on domain-specific benchmarks.
"""
results = {}
for benchmark_name, dataset in benchmark_datasets.items():
correct = 0
total = 0
for example in dataset:
problem = example["problem"]
ground_truth = example["ground_truth"]
# Generate solution
reasoning = adapted_model.generate(
problem,
max_tokens=2000
)
# Extract final answer and check correctness
extracted_answer = extract_answer(reasoning)
if extracted_answer == ground_truth:
correct += 1
total += 1
accuracy = correct / total * 100
results[benchmark_name] = accuracy
print(f"{benchmark_name}: {accuracy:.1f}%")
return results
Hyperparameters:
When to Use:
When NOT to Use: