# Deepseek Math V2 Self Verifiable

> Synergistic verifier-generator training loop enabling LLMs to identify logical issues in mathematical proofs without reference solutions, improving reasoning rigor through meta-verification. Apply when you need to scale mathematical reasoning without hand-labeled proof annotations.

- Skill: `adu2021/deepseek-math-v2-self-verifiable` (Agent Skill)
- Install (CLI): `npx skillmds@latest add adu2021/deepseek-math-v2-self-verifiable`
- Raw SKILL.md: https://api.skillmd.com/api/skills/adu2021/deepseek-math-v2-self-verifiable/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: adu2021 (https://skillmd.com/u/adu2021)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/adu2021/deepseek-math-v2-self-verifiable

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## Summary

DeepSeekMath-V2 introduces a self-verifiable mathematical reasoning system through an iterative training cycle between proof verification and generation components. Instead of using final-answer rewards, the approach directly addresses proof rigor by training an LLM-based verifier that identifies logical issues and a generator that performs self-verification, creating synergistic improvement cycles.

## Core Technique

The system operates through three coupled components:

1. **Verifier Training:** Train an LLM to identify logical issues in proofs using meta-verification—where the verifier itself must identify which rubrics it should evaluate against. This eliminates the need for reference solutions or ground-truth proof steps.

2. **Generator Self-Verification:** Train the proof generator to analyze its own work using the same rubrics as the external verifier, enabling deliberate self-refinement during generation rather than blind trial-and-error.

3. **Verification-Scaled Labeling:** Use the verifier to automatically label challenging proofs, creating training data that progressively improves both components in a reinforcement cycle.

## Implementation

**Meta-verification:** The verifier outputs both a proof evaluation and the reasoning criteria it applied. This self-aware approach ensures the verifier produces faithful analyses rather than hallucinated critiques.

**Synergistic scaling:** Allocate compute to verification as a first-class post-training component, not just as an evaluation metric. Scaling verification compute directly improves the verifier's ability to label and improve the generator.

**Iterative refinement:** Cycle between: (1) generator creates proofs with self-verification, (2) external verifier identifies remaining issues, (3) both components retrain on improved data, (4) repeat.

## When to Use

- Training mathematical reasoning models for high-stakes applications (olympiad math, formal verification)
- Scaling reasoning training without expensive human proof annotations
- Building verifiable reasoning pipelines where proof correctness matters more than speed
- Improving model accuracy on complex multi-step mathematical problems

## When NOT to Use

- Tasks where final-answer accuracy is sufficient without proof correctness
- Simple computation-heavy problems better served by symbolic solvers
- Scenarios with abundant high-quality proof annotations (pure supervised learning may be simpler)
- Real-time reasoning where iterative verification adds too much latency

## Key References

- Meta-verification concepts for self-aware model evaluation
- Proof rubrics and automated mathematical correctness assessment
- Iterative reinforcement learning for verification-guided generation