# Demystifying Domain-adaptive Post-training for Financial LLMs

arXiv: https://arxiv.org/abs/2501.04961 · HTML: https://mlsanity.com/paper/2501.04961/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 64 of its 95 references that are in the map.
- First posted on arXiv 9 Jan 2025.

## Builds on (most cited first, 30 of 64)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
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- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [Let's Verify Step by Step](https://mlsanity.com/paper/2305.20050/) (2023)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [PIQA: Reasoning about Physical Commonsense in Natural Language](https://mlsanity.com/paper/1911.11641/) (2019)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering](https://mlsanity.com/paper/1809.02789/) (2018)
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale](https://mlsanity.com/paper/2406.17557/) (2024)
- [Large Language Models Cannot Self-Correct Reasoning Yet](https://mlsanity.com/paper/2310.01798/) (2023)
- [Enhancing Chat Language Models by Scaling High-quality Instructional Conversations](https://mlsanity.com/paper/2305.14233/) (2023)
- [Crowdsourcing Multiple Choice Science Questions](https://mlsanity.com/paper/1707.06209/) (2017)
- [Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies](https://mlsanity.com/paper/2101.02235/) (2021)
- [WizardCoder: Empowering Code Large Language Models with Evol-Instruct](https://mlsanity.com/paper/2306.08568/) (2023)
- [MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms](https://mlsanity.com/paper/1905.13319/) (2019)
- [Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems](https://mlsanity.com/paper/1705.04146/) (2017)
- [FinQA: A Dataset of Numerical Reasoning over Financial Data](https://mlsanity.com/paper/2109.00122/) (2021)
- [Zephyr: Direct Distillation of LM Alignment](https://mlsanity.com/paper/2310.16944/) (2023)

## Built on by (most cited first, 24 of 24)

- [A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems](https://mlsanity.com/paper/2504.09037/) (2025)
- [MAS-ZERO: Designing Multi-Agent Systems with Zero Supervision](https://mlsanity.com/paper/2505.14996/) (2025)
- [Does Context Matter? ContextualJudgeBench for Evaluating LLM-based Judges in Contextual Settings](https://mlsanity.com/paper/2503.15620/) (2025)
- [Standard Benchmarks Fail -- Auditing LLM Agents in Finance Must Prioritize Risk](https://mlsanity.com/paper/2502.15865/) (2025)
- [MetaSynth: Meta-Prompting-Driven Agentic Scaffolds for Diverse Synthetic Data Generation](https://mlsanity.com/paper/2504.12563/) (2025)
- [Domain Adaptation of Foundation LLMs for e-Commerce](https://mlsanity.com/paper/2501.09706/) (2025)
- [CEQuest: Benchmarking Large Language Models for Construction Estimation](https://mlsanity.com/paper/2508.16081/) (2025)
- [FinCoT: Grounding Chain-of-Thought in Expert Financial Reasoning](https://mlsanity.com/paper/2506.16123/) (2025)
- [CodeReasoner: Enhancing the Code Reasoning Ability with Reinforcement Learning](https://mlsanity.com/paper/2507.17548/) (2025)
- [FinWorld: An All-in-One Open-Source Platform for End-to-End Financial AI Research and Deployment](https://mlsanity.com/paper/2508.02292/) (2025)
- [Urban-MAS: Human-Centered Urban Prediction with LLM-Based Multi-Agent System](https://mlsanity.com/paper/2511.00096/) (2025)
- [NAACL2025 Tutorial: Adaptation of Large Language Models](https://mlsanity.com/paper/2504.03931/) (2025)
- [Exploring the Synergy of Quantitative Factors and Newsflow Representations from Large Language Models for Stock Return Prediction](https://mlsanity.com/paper/2510.15691/) (2025)
- [WeMusic-Agent: Efficient Conversational Music Recommendation via Knowledge Internalization and Agentic Boundary Learning](https://mlsanity.com/paper/2512.16108/) (2025)
- [Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Models](https://mlsanity.com/paper/2605.24564/) (2026)
- [FinGuard: Detecting Financial Regulatory Non-Compliance in LLM Interactions](https://mlsanity.com/paper/2605.29427/) (2026)
- [CACHE-UK: A Stability-Aware Memory Editor for Sequentially Updated Quantized LLMs in Finance](https://mlsanity.com/paper/2607.28292/) (2026)
- [DKL: Decoupled Knowledge Learning for Instruction-Tuned Language Models](https://mlsanity.com/paper/2609.02685/) (2026)
- [Less Data Approximates More: Earning Faithful Confidence in High-Stakes Domains](https://mlsanity.com/paper/2604.08454/) (2026)
- [TELLME: Test-Enhanced Learning for Language Model Enrichment](https://mlsanity.com/paper/2608.11788/) (2026)
- [Reading the News: Adapting Large Language Models to Swedish Journalism Through Continued Pre-Training](https://mlsanity.com/paper/2608.30609/) (2026)
- [KItCAT: Knowledge Injection via Input Corruption for Auto-regressive Training](https://mlsanity.com/paper/2609.00082/) (2026)
- [SALUTE: Benchmarking and Adapting LLMs for the Defense Domain](https://mlsanity.com/paper/2609.15022/) (2026)
- [Self-Evolving Algorithm-Design Agents: Escaping In-Context Evolutionary Stagnation via Population-Curated Policy Optimization](https://mlsanity.com/paper/2609.38757/) (2026)

## Abstract (arXiv)

Domain-adaptive post-training of large language models (LLMs) has emerged as a promising approach for specialized domains such as medicine and finance. However, significant challenges remain in identifying optimal adaptation criteria and training strategies across varying data and model configurations. To address these challenges, we introduce FINDAP, a systematic and fine-grained investigation into domain-adaptive post-training of LLMs for the finance domain. Our approach consists of four key components: FinCap, which defines the core capabilities required for the target domain; FinRec, an effective training recipe that jointly optimizes continual pre-training and instruction-following, along with a novel preference data distillation method leveraging process signals from a generative reward model; FinTrain, a curated set of training datasets supporting FinRec; and FinEval, a comprehensive evaluation suite aligned with FinCap. The resulting model, Llama-Fin, achieves state-of-the-art performance across a wide range of financial tasks. Our analysis also highlights how each post-training stage contributes to distinct capabilities, uncovering specific challenges and effective solutions, providing valuable insights for domain adaptation of LLMs