• 🤖 Oct 2025: Llama-Fin-8b model released on 🤗 Hugging Face - state-of-the-art financial LLM!
• 🏋️ Oct 2025: FinTrain training dataset released on 🤗 Hugging Face - comprehensive training data for financial LLMs!
• 🎉 Sep 2025: Check our Post-training Research Hub for comprehensive resources including FinDAP, tutorials, RAG, and continual pre-training!
• 🔧 Sep 2025: FinRec training code is now available! Train your own domain-specific financial LLMs with our proven recipes.
• 📊 Jan 2025: FinEval benchmark released on 🤗 Hugging Face - comprehensive evaluation suite for financial LLMs!

Given a pre-trained LLM with strong general capabilities (e.g., Llama3-8b-instruct), how can we effectively adapt it to a target domain (e.g., finance) through post-training?

Key Questions We Address:

• ❓ What criteria are desirable for successful adaptation?
• ❓ What are the most effective training recipes with respect to data and model?
• ❓ How do different post-training stages contribute to domain expertise?

FinDAP is a comprehensive finance-specific post-training framework that includes:

• 🎯 Systematic capability identification for financial LLMs
• 📊 State-of-the-art evaluation framework (FinEval)
• 🔧 Advanced training recipes with novel preference alignment
• 🏆 High-performance model checkpoints (Llama-Fin)

We use the finance domain as a case study to demonstrate effective domain-adaptive post-training on instruction-tuned LLMs.

Figure 1: FinDAP framework overview

Our framework consists of four key components:

We systematically identify the essential capabilities for domain-specific LLMs, focusing on four fundamental areas that enable effective financial domain adaptation:

🔍 Detailed Capability Analysis

📝 Note: While domains may vary in sensitivity (e.g., medical vs. entertainment) and multi-modality requirements, we focus on these four core capabilities as the foundation for effective domain adaptation. Future work may explore additional aspects such as multi-modal integration and domain-specific ethical considerations.

FinRec provides our complete training methodology for domain-adaptive post-training, featuring joint optimization of continual pre-training and instruction tuning, plus novel preference alignment techniques.

🏗️ Training Pipeline Overview

Our training recipe consists of three progressive stages:

🚀 Quick Start Training Guide

Step 1: Environment Setup 📦

# Create and activate conda environment
conda create -n FinDAP python=3.10 && conda activate FinDAP

# Install dependencies
pip install -r requirements.txt

Step 2: Joint CPT + SFT Training ⚙️

Joint Continual Pre-training and Supervised Fine-tuning with curriculum learning:

# Foundation curriculum - basic financial concepts and tasks
./scripts/cpt_sft/mix_cpt_mix_sft_extend_book_exercise_downsample_from_base.sh

# Advanced curriculum - complex reasoning and specialized tasks
./scripts/cpt_sft/mix_cpt_mix_sft_extend_book_exercise_downsample_from_v1.sh

Step 3: Preference Alignment with Offline RL 🎯

Revolutionary preference learning using both outcome and process signals:

# Offline RL with final answer preference and stepwise corrective preference
./scripts/offline_rl/rpo_cfa_stepwise.sh

Novel Features:

• 🎯 Dual Signal Learning: Outcome-based + process-based preference optimization
• 🔄 Stepwise Correction: Fine-grained error correction during reasoning
• 🤖 Generative Reward Model: On-policy trajectory construction
• 📈 RPO Algorithm: Robust Policy Optimization for financial domain

🏆 Key Training Innovations

The culmination of our FinDAP framework is Llama-Fin, a state-of-the-art financial LLM that achieves exceptional performance across diverse financial tasks.

🎯 Model Highlights

• 🥇 State-of-the-art Performance: Leading results on financial benchmarks
• 🎯 8B Parameter Efficiency: Optimal balance of performance and computational efficiency
• 🧠 Multi-capability Excellence: Strong performance across concepts, tasks, reasoning, and instruction following
• 📈 Novel Contributions: Demonstrates effectiveness of dual preference learning and joint CPT+SFT training

📊 Performance Note: Detailed results and comparisons available in our paper and evaluation using FinEval.

Our evaluation framework provides a comprehensive assessment of the four core capabilities through carefully curated development and held-out evaluation sets.

Figure 2: FinEval evaluation framework • Comprehensive assessment across multiple dimensions and task types
New datasets released with FinDAP are highlighted

📈 Evaluation Dimensions

Our evaluation framework assesses models across multiple orthogonal dimensions:

🏆 Key Features

• ✅ Multi-dimensional Assessment: Orthogonal evaluation across task types, categories, and methods
• ✅ Development & Held-out Sets: Proper train/test split for reliable evaluation
• ✅ Novel Task Generalization: Assessment on completely unseen task categories
• ✅ Reasoning Evaluation: Both direct answers and step-by-step reasoning assessment
• ✅ Comprehensive Coverage: Aligned with FinCap capabilities framework

🚀 Quick Start Guide

FinEval integrates seamlessly with standard evaluation frameworks. We recommend using LLM Evaluation Harness for effortless dataset loading and evaluation.

Step 1: Environment Setup 📦

# Use existing FinDAP environment or create new one
conda create -n FinDAP python=3.10 && conda activate FinDAP

# Clone the evaluation harness
git clone --depth 1 https://github.com/EleutherAI/lm-evaluation-harness
cd lm-evaluation-harness
pip install -e .

# Install additional evaluation dependencies
pip install datasets transformers vllm

Step 2: Configuration ⚙️

Create a task configuration file (example: cfa-challenge.yaml):

task: cfa-challenge
dataset_path: Salesforce/FinEval
dataset_name: CFA-Challenge
output_type: generate_until
test_split: test
doc_to_text: query
doc_to_target: answer
should_decontaminate: true
doc_to_decontamination_query: query
generation_kwargs:
  until:
    - "</s>"
    - "<|im_end|>"
    - "<|eot_id|>"
    - "<|end_of_text|>"
    - "<|end|>"
    - "<|endoftext|>"
  do_sample: false
  temperature: 0.0
  max_gen_toks: 8000
metric_list:
  - metric: exact_match
    aggregation: mean
    higher_is_better: true
    ignore_case: true
    ignore_punctuation: true
metadata:
  version: 1.0

Step 3: Evaluation 🎯

# Login to Hugging Face
huggingface-cli login --token {YOUR_HF_TOKEN}

# Set environment variables
export HF_DATASETS_CACHE={YOUR_CACHE_LOC}
export TRANSFORMERS_CACHE={YOUR_CACHE_LOC}
export TRUST_REMOTE_CODE=1

# Configure evaluation
system_prompt="Please act as a CFA exam taker and evaluate the given scenario to choose the most appropriate answer from options A, B, and C. Start by offering a brief explanation of your thought process and reasoning, up to 100 words. After the explanation, select your answer using the format: 'Selection: [[A]]' (e.g., 'Explanation: (your explanation)\nSelection: [[A]]'). If you find no answer is correct, directly mention it"

model="Salesforce/Llama-Fin-8b"

# Run evaluation
lm_eval --apply_chat_template --model vllm --log_samples --write_out \
  --model_args pretrained=${model},max_length=8000,dtype=bfloat16,trust_remote_code=True,tensor_parallel_size={YOUR_NUM_GPU},gpu_memory_utilization=0.6 \
  --system_instruction "$system_prompt" \
  --tasks cfa-challenge \
  --device cuda \
  --output_path {YOUR_OUTPUT_LOC} \
  --batch_size auto \
  --num_fewshot 0

🔬 Research Purpose Only: This release supports academic research as described in our EMNLP 2025 paper.

This release is for research purposes only in support of an academic paper. Our datasets and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before model deployment. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our Salesforce AUP and AI AUP

If you find our project helpful, please consider citing our paper 😊

@misc{ke2025demystifyingdomainadaptiveposttrainingfinancial,
      title={Demystifying Domain-adaptive Post-training for Financial LLMs}, 
      author={Zixuan Ke and Yifei Ming and Xuan-Phi Nguyen and Caiming Xiong and Shafiq Joty},
      year={2025},
      eprint={2501.04961},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2501.04961}, 
}