so8t-thinking-plan-mode - SKILL.md Agent Skill

name: so8t-thinking-plan-mode description: Develop comprehensive evolution plan for Qwen2.5-7B to SO8T/thinking model with advanced Japanese capabilities, mathematical reasoning at Nobel/Fields medal level, and integration of 2024-2026 LLM breakthroughs using Moonshot pipeline as Sunset pipeline foundation. Use when planning SO8T model evolution, Japanese capability enhancement, mathematical reasoning advancement, or integrating cutting-edge LLM research.

SO8T Thinking Plan Mode

Overview

This skill creates comprehensive evolution plans for transforming Qwen2.5-7B into an advanced SO8T/thinking model with Nobel/Fields medal-level reasoning capabilities. The plan integrates multiple Japanese datasets, scientific/mathematical reasoning enhancement, and 2024-2026 LLM breakthroughs, using Moonshot AI pipeline principles adapted as "Sunset Pipeline".

Core Capabilities

1. Japanese Language Enhancement

Base Research: Continual Pre-Training for Cross-Lingual LLM Adaptation (COLM 2024)

Vocabulary Expansion: Extend Llama-style vocabularies with Japanese characters
Continual Pre-Training: Train on 100B+ Japanese web corpora after English pre-training
Parallel Corpora Integration: Enhance translation and cross-lingual transfer
Expected Improvement: 70%+ performance gain on Japanese tasks

2. Mathematical Reasoning Advancement

Key Techniques:

AgenticMath Pipeline: 4-stage agentic data generation (filtering → rephrasing → augmentation → evaluation)
Background Operators: Prolog-based formal reasoning with mathematical predicates
Chain of Self-Correction (CoSC): Iterative validation and refinement
Step Guided Reasoning: Training-free reflection framework
Target Performance: Nobel/Fields medal level (IMO silver/gold equivalent)

3. Advanced Reasoning Integration

AlphaProof/ArisTotLe Approach:

Reinforcement Learning at Scale: Train on auto-formalized mathematical problems
Formal Verification: Lean-based grounded reasoning with correctness guarantees
Multi-Component Architecture: Proof search + informal reasoning + geometry solvers
Test-Time RL: Generate problem variants during inference for adaptation

4. 2024-2026 LLM Breakthroughs Integration

Data Quality & Efficiency

Phi-4 Approach: High-quality curated data over scale
14B parameters achieving strong results through data-centric training

System 2 Reasoning

Meta Chain-of-Thought (Meta-CoT): Process supervision + synthetic data + search algorithms
Human-like reasoning through explicit process modeling

Multimodal Enhancement

NVLM 1.0 Architecture: Decoder-only + cross-attention hybrid
1-D tile-tagging for high-resolution processing
Quality over scale in dataset curation

Open-Source Excellence

TeleChat2/2.5 + T1: 10T high-quality tokens + SFT + DPO + RL
Specialized reasoning outperforming proprietary models

Sunset Pipeline Architecture

Phase 1: Foundation Enhancement (Japanese Capability)

# Continual Pre-Training Pipeline
class SunsetFoundation:
    def enhance_japanese_capability(self, base_model, japanese_corpus):
        # Vocabulary expansion
        expanded_vocab = self.expand_vocabulary(base_model.vocab, japanese_chars)

        # Continual pre-training
        enhanced_model = self.continual_pretrain(
            base_model, japanese_corpus, max_tokens=100e9
        )

        # Parallel data integration
        bilingual_model = self.integrate_parallel_corpora(
            enhanced_model, bilingual_datasets
        )

        return bilingual_model

Phase 2: Mathematical Reasoning Development

# AgenticMath + Formal Reasoning Pipeline
class SunsetReasoning:
    def develop_mathematical_capability(self, enhanced_model, math_datasets):
        # AgenticMath data generation
        agentic_data = self.generate_agentic_math_data(
            seed_questions=50000,
            stages=['filter', 'rephrase', 'augment', 'evaluate']
        )

        # Background operators training
        prolog_corpus = self.create_math_prolog_corpus(math_datasets)
        formal_model = self.train_background_operators(
            enhanced_model, prolog_corpus, k_fold_validation=True
        )

        # CoSC integration
        self_correcting_model = self.integrate_chain_of_self_correction(
            formal_model, correction_stages=3
        )

        return self_correcting_model

Phase 3: Advanced Reasoning Integration

# AlphaProof/ArisTotLe Style Pipeline
class SunsetAdvancedReasoning:
    def integrate_nobel_fields_reasoning(self, reasoning_model, formal_datasets):
        # Auto-formalization pipeline
        formalized_problems = self.auto_formalize_problems(
            raw_problems=formal_datasets,
            language='lean'
        )

        # RL training at scale
        rl_agent = self.train_rl_agent(
            model=reasoning_model,
            formalized_problems=formalized_problems,
            scale='millions_of_problems'
        )

        # Multi-component architecture
        integrated_model = self.build_multi_component_architecture(
            rl_agent=rl_agent,
            proof_search_system='lean_search',
            informal_reasoner='llm_based',
            geometry_solver='specialized'
        )

        return integrated_model

Phase 4: Cutting-Edge LLM Integration

# 2024-2026 Breakthroughs Integration
class SunsetInnovation:
    def integrate_modern_breakthroughs(self, advanced_model):
        # Data quality focus (Phi-4 approach)
        quality_model = self.implement_data_centric_training(
            model=advanced_model,
            curated_datasets='high_quality_only',
            scale_efficient=True
        )

        # System 2 reasoning (Meta-CoT)
        system2_model = self.implement_meta_chain_of_thought(
            model=quality_model,
            process_supervision=True,
            synthetic_data_generation=True
        )

        # Multimodal enhancement (NVLM approach)
        multimodal_model = self.integrate_multimodal_capabilities(
            model=system2_model,
            architecture='decoder_cross_attention_hybrid',
            image_processing='1d_tile_tagging'
        )

        return multimodal_model

Phase 5: Specialization and Optimization

# TeleChat2/T1 Style Specialization
class SunsetSpecialization:
    def create_specialized_reasoning(self, integrated_model):
        # Massive pre-training continuation
        specialized_model = self.continue_pretraining(
            model=integrated_model,
            tokens=10e12,  # 10 trillion tokens
            quality_focus=True
        )

        # Multi-stage post-training
        sft_model = self.supervised_fine_tuning(
            model=specialized_model,
            reasoning_datasets='comprehensive'
        )

        dpo_model = self.direct_preference_optimization(
            model=sft_model,
            preference_data='reasoning_preferences'
        )

        final_model = self.reinforcement_learning_finetuning(
            model=dpo_model,
            reward_functions='reasoning_quality'
        )

        return final_model

Planning Workflow

Step 1: Research and Analysis

1.1 Analyze current Qwen2.5-7B capabilities
1.2 Survey available Japanese datasets
1.3 Identify mathematical reasoning gaps
1.4 Review 2024-2026 LLM breakthroughs
1.5 Assess Moonshot pipeline applicability

Step 2: Data Strategy Development

2.1 Curate Japanese capability datasets
2.2 Develop mathematical reasoning datasets
2.3 Create formal reasoning corpora
2.4 Prepare multimodal training data
2.5 Design data quality pipelines

Step 3: Architecture Design

3.1 Design continual pre-training approach
3.2 Plan agentic data generation systems
3.3 Define multi-component reasoning architecture
3.4 Design RL training pipelines
3.5 Plan evaluation methodologies

Step 4: Implementation Roadmap

4.1 Phase 1: Foundation Enhancement (2-4 weeks)
4.2 Phase 2: Mathematical Reasoning (4-6 weeks)
4.3 Phase 3: Advanced Reasoning (6-8 weeks)
4.4 Phase 4: Innovation Integration (4-6 weeks)
4.5 Phase 5: Specialization (3-4 weeks)

Step 5: Evaluation and Validation

5.1 Define success metrics
5.2 Plan benchmarking strategy
5.3 Design validation protocols
5.4 Prepare deployment procedures

Key Research Findings Integration

Japanese Capability Enhancement

Continual Pre-Training: 70%+ improvement on Japanese tasks
Vocabulary Expansion: Efficient token utilization
Parallel Corpora: Enhanced cross-lingual transfer
Resource Efficiency: 9x English-Japanese disparity addressed

Mathematical Reasoning Advancement

AgenticMath: 30-60K samples achieve baseline performance
Formal Reasoning: 84.8% accuracy on MATH-Prolog
CoSC: 53.5% on MATH dataset
Step Guided Reasoning: 27.1% → 36.3% improvement

Advanced Reasoning Capabilities

AlphaProof: IMO silver medal equivalent
ArisTotLe: IMO gold medal equivalent
RL at Scale: Progressive problem difficulty solving
Formal Verification: Correctness guarantees

2024-2026 Breakthroughs

Data Quality Focus: Phi-4's curated data approach
System 2 Reasoning: Meta-CoT's process modeling
Multimodal Integration: NVLM's hybrid architecture
Open-Source Excellence: TeleChat2/T1's comprehensive training

Success Metrics

Japanese Capabilities

JGLUE Benchmark: Top-tier performance
Japanese QA Tasks: 70%+ improvement
Translation Quality: BLEU score > 40
Cultural Understanding: Domain-specific accuracy > 85%

Mathematical Reasoning

MATH Dataset: Pass rate > 45%
IMO Problems: Silver medal equivalent
Formal Proofs: Lean verification success > 80%
Multi-step Reasoning: Complex problem solving

Advanced Reasoning

ArXiv-Level Tasks: Citation accuracy > 90%
Biorxiv Analysis: Scientific reasoning quality
Nobel Fields Level: Breakthrough concept understanding
Cross-Domain Transfer: Knowledge application flexibility

Overall Performance

Multimodal Capabilities: GPT-4o competitive performance
Long Context: 128K+ token handling
Agentic Intelligence: Multi-step tool use
Efficiency: Parameter-efficient scaling

Risk Mitigation

Technical Risks

Overfitting: Validation on held-out datasets
Catastrophic Forgetting: Continual learning techniques
Computational Costs: Efficient training strategies
Data Quality: Rigorous curation and validation

Research Risks

Novelty vs. Stability: Balanced innovation approach
Reproducibility: Comprehensive documentation
Ethical Considerations: Responsible AI development
Bias Mitigation: Diverse data representation

Timeline and Milestones

Month 1-2: Foundation Phase

Japanese capability enhancement implementation
Initial continual pre-training setup
Baseline performance establishment

Month 3-5: Reasoning Development

Mathematical reasoning pipeline development
AgenticMath data generation
Formal reasoning integration

Month 6-9: Advanced Capabilities

AlphaProof/ArisTotLe approach implementation
RL training pipeline development
Multi-component architecture integration

Month 10-12: Innovation Integration

2024-2026 breakthroughs implementation
System 2 reasoning development
Multimodal capabilities enhancement

Month 13-15: Specialization and Optimization

Domain-specific fine-tuning
Performance optimization
Final evaluation and validation

Resource Requirements

Computational Resources

GPU Clusters: 16+ A100/H100 GPUs for training
Memory: 2TB+ RAM for large model handling
Storage: 50TB+ for datasets and checkpoints

Data Resources

Japanese Corpora: 100B+ tokens web data
Mathematical Datasets: Formalized problem collections
Multimodal Data: High-quality image-text pairs
Research Papers: ArXiv/Biorxiv access

Human Resources

ML Engineers: 3-5 senior engineers
Research Scientists: 2-3 PhD-level researchers
Domain Experts: Mathematics and Japanese language specialists

Budget Estimation

Development Costs

Compute: $500K-$1M (cloud GPU costs)
Data Curation: $100K-$200K (dataset preparation)
Personnel: $400K-$600K (15-month development)
Infrastructure: $50K-$100K (storage and tools)

Total Project Cost: $1.05M-$1.9M

Conclusion

This comprehensive plan transforms Qwen2.5-7B into a state-of-the-art SO8T/thinking model with Nobel/Fields medal-level reasoning capabilities. By integrating 2024-2026 LLM breakthroughs and adapting Moonshot AI's successful pipeline as "Sunset Pipeline", the project achieves:

Japanese Language Mastery: Industry-leading Japanese capabilities
Mathematical Excellence: Nobel/Fields medal equivalent reasoning
Scientific Advancement: ArXiv/Biorxiv-level research comprehension
Future-Proof Architecture: 2024-2026 breakthrough integration
Scalable Infrastructure: Enterprise-grade deployment readiness

The Sunset Pipeline represents a systematic approach to achieving AGI-level reasoning capabilities while maintaining practical deployment feasibility.