Service - AI Coding

Code RLHF

Software engineers evaluate AI-generated code on correctness, security, readability, efficiency, and style. Specialist annotator pools by language. Automated unit test execution and security linting combined with expert human judgment the most rigorous code preference dataset you can build.

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5+
Evaluation dimensions per code pair correctness, security, readability, efficiency, style
10
Programming languages with specialist annotator pools available
Auto+Human
Automated test execution and security linting alongside human judgment
5yr+
Minimum engineering experience required for code annotators not generalists
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Correctness TestingSecurity ReviewCode ReadabilityAlgorithm EfficiencyStyle ConsistencyUnit Test ExecutionSAST AnalysisMulti-Language SupportCorrectness TestingSecurity ReviewCode ReadabilityAlgorithm EfficiencyStyle Consistency
Code RLHF Quality Comparison
✗ RESPONSE A · REJECTED
✓ RESPONSE B · PREFERRED
● THREAD SAFE
● CORRECTNESS: 9.1/10
▼ CODE QUALITY COMPARISON
RESP ANEUTRALRESP B ▶
✓ 5,800 CODE PAIRS · IAA 0.82
What It Is

Engineers evaluating AI-generated code

Code RLHF is preference data specifically for training AI coding assistants and code generation models. It requires annotators who can actually run code, reason about edge cases, spot security vulnerabilities, and compare algorithmic efficiency none of which a general-purpose annotator can do reliably.

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Live Annotation Interface

Code RLHF Quality Comparison Tool

Senior engineers compare two AI-generated code solutions, evaluating correctness, edge case handling, readability, and security selecting the preferred output for reward model training.

ConcaveLabel Studio Code RLHF · Task: Implement rate limiter · Engineer: Arjun K.
Response A REJECTED
def rate_limit(requests, limit): count = 0 for r in requests: if count < limit: process(r) count += 1 # TODO: add time window
NO TIME WINDOW MISSING RESET NO THREAD SAFETY READABLE
Response B PREFERRED ✓
import time from threading import Lock class TokenBucketLimiter: def __init__(self, rate, per): self.rate = rate self.per = per self.tokens = rate self.last = time.time() self._lock = Lock() def allow(self) -> bool: with self._lock: now = time.time() delta = now - self.last self.tokens = min( self.rate, self.tokens + delta * self.rate / self.per ) self.last = now if self.tokens >= 1: self.tokens -= 1 return True return False
TOKEN BUCKET THREAD SAFE SMOOTH REFILL PRODUCTION READY
Tasks Compared:5,800 pairs Languages:Python / JS / Go / Rust Engineers:14 Senior SWEs IAA κ:0.82
How It Works

Three things the pipeline does on every Code RLHF project

Multi-dimension code evaluation
Responses scored across correctness, efficiency, security, style, and explainability, not binary pass/fail. Per-dimension quality signals produce richer reward model training data than single-score preference labeling.
Execution-verified preference pairs
Where possible, responses are executed against test cases before preference ranking. Execution correctness is one input to the final preference judgment as runtime behavior is verified, not just assumed from reading.
Security and vulnerability scanning
Code responses scanned for known vulnerability patterns (OWASP, CWE), injection risks, and insecure practices before entering the preference dataset. Security failures flagged with severity and CWE reference.
Pipeline Capabilities

What the infrastructure delivers

Execution-Verified Correctness
The pipeline runs sandboxed test execution on every code pair before human review there are automated signals, not just impressions, inform preference decisions.
Multi-Dimension Quality Scoring
Each pair receives independent scores on correctness, security, readability, efficiency, and style. Your reward model learns to distinguish quality at the dimension level, not just overall.
Training-Ready Delivery
Pairs delivered in JSONL with chosen/rejected code, per-dimension scores, engineer rationale, and automated scan results which are compatible with TRL, Axolotl, and OpenRLHF.
Evaluation Dimensions

Five dimensions, independently scored per pair

Each preference pair receives a score on all five dimensions so your reward model learns to distinguish between quality improvements in specific areas not just overall "better."

✅
Correctness
Does the code do what was asked? Handles edge cases (null, empty, large inputs, boundary values)? Verified by automated test execution + human logic review.
🔒
Security
SQL injection, XSS, CSRF, path traversal, insecure random, hardcoded secrets, input validation gaps. Automated SAST + senior engineer security review.
📖
Readability
Naming clarity, function decomposition, comment appropriateness, code structure. Would a mid-level engineer understand this without a walkthrough?
⚡
Efficiency
Time complexity (Big-O), space complexity, unnecessary database calls, redundant loops, missed vectorisation opportunities. Algorithm choice relative to input scale.
🎨
Style
Conforms to language idioms (Pythonic, idiomatic JS), consistent formatting, appropriate use of language features, follows the specified style guide (PEP8, Airbnb, Google).
What You Get

Annotated code preference data backed by verifiable quality proof

Every Code RLHF project delivers three core outputs alongside the preference dataset.

Preference Dataset
Pairwise and ranked code response preference data in your preferred format (JSONL, Parquet, HuggingFace dataset). Includes per-pair scores across correctness, security, efficiency, and style dimensions, not just a binary preference label.
QA Report with Kappa
Per-language Cohen's kappa, per-dimension agreement scores, execution test results, and security scan findings. Annotator agreement reported separately per programming language and task type, not averaged across the full set.
Data Card & Annotation Guide
Full ML data card documenting languages covered, annotation rubric, execution environment, security scan tooling, known edge cases, and quality thresholds applied. Includes the prompt set used for evaluation.
Pricing

Per-pair pricing,
language-based tiers

Code RLHF is priced per preference pair based on language, complexity of the coding task, and whether security review is included as a primary dimension.

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Python / JS / general (Tier 1)$5–8 / pair
Go / Rust / C++ / systems (Tier 2)$7–12 / pair
Security-primary review (Tier 3)$12–21 / pair
SQL / database / query pairs$4–7 / pair
Minimum project size300 pairs
Free audit20 pairs / $0

Get 20 code pairs evaluated free

Send us 20 of your AI model's code output pairs. We will run them through our engineer review pipeline and return per-dimension scores, rationale, and a quality baseline no cost, no commitment.

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