Self-Rewarding Language Models for Open-Ended Market Scenario Simulation
In this article, we explore Self-Rewarding Language Models (SRLMs) and their potential to produce open-ended, plausible financial scenarios by learning internal reward signals. We compare them to GANs and diffusion models, and examine how they could support stress testing, strategy development, and risk analysis in finance.
In 2023, a hedge fund PM told me:
“If only we could generate market scenarios that go beyond historical data – something truly new, yet plausible. That would change the game.”
At the time, it seemed an unattainable vision. Most synthetic data approaches – from bootstrapping to GANs – remain trapped within the statistical structure of historical data, unable to invent truly new, yet coherent, market scenarios.
Recent breakthroughs in generative AI suggest this might no longer be wishful thinking.
Enter self-rewarding language models (SRLMs).
Originally designed for natural language generation, SRLMs unlock a powerful capability: models that generate, evaluate, and improve their own outputs without external reward models or human reinforcement. Imagine applying this to finance – where defining “correct” is rarely binary and innovation thrives on exploring the unobserved [1].
Why Real Data Alone Falls Short
Backtesting, stress testing, and scenario generation all rely on the same source: historical market data. Yet history offers only a single realisation of a complex process.
✅ Tail events are sparse. Your dataset may span multiple crises, but the variety and frequency of true extremes remain insufficient for robust tail risk modelling.
✅ Macro regime coverage is incomplete. Structural breaks, such as negative rates combined with high inflation, may never have occurred in your data window.
✅ Generalisation is limited. Strategies tested only on historical data risk underperforming in novel market conditions.
For decades, quants have tried to solve this with bootstrapping, noise injection, stochastic processes, and more recently, GANs and diffusion models. While these expand the dataset, they rarely expand the set of possible futures meaningfully. For example, JP Morgan’s work on diffusion models for financial time series demonstrates realism within historical structures but does not create unobserved market futures [2].
What Are Self-Rewarding Language Models?
In their recent paper, researchers from Meta and NYU proposed self-rewarding language models [1].
Here’s how they work:
Generate multiple candidate outputs. For example, a language model produces several answers to a question or multiple story endings.
Internally rank these candidates. Instead of relying on external human feedback or reward models, SRLMs use an internal scoring mechanism – learned during training – to determine which output is best.
Iteratively self-improve. The model refines its generation strategy by learning from its own rankings, enhancing output quality over time.
Originally developed for open-ended text generation, this framework translates powerfully to financial data generation, where “correct” often means plausible, coherent, and economically consistent rather than identical to history.
Self-Rewarding Model Workflow for Synthetic Market Data
Potential Applications in Finance
Imagine a model that can:
💡 Generate synthetic market scenarios unbounded by historical data, exploring combinations of macro factors and market dynamics never seen before.
💡 Self-rank and refine these scenarios, learning what constitutes plausible market behaviour without requiring externally defined reward models (which are notoriously difficult to design for finance).
💡 Support open-ended stress testing and robust strategy development, enabling portfolio managers to prepare for futures the market has not yet revealed.
Related concepts in finance include:
Conditional GANs for financial scenario generation [3], which still rely on supervised objectives and external realism scores.
Generative market simulation models [4] proposing market simulators with explicit conditioning but not open-ended self-rewarding frameworks.
Diffusion-based models, such as CoDi [5], focusing on efficient conditioning rather than unconstrained scenario creation.
But There Are Challenges
⚠️ Defining internal rewards for financial realism is complex. In NLP, models learn preferences from large-scale text corpora. In finance, realism requires respecting market microstructure, statistical properties (skew, kurtosis, auto-correlation structures), and macroeconomic constraints.
⚠️ Computational costs are high. Generating and ranking multiple candidates increases inference time and resource requirements.
⚠️ Mode collapse risks remain. If the internal reward system is poorly aligned with economic plausibility, outputs may converge to trivial or unrealistic solutions.
How Does This Compare to Other Generative Approaches?
Approach | Strength | Limitation |
|---|---|---|
GANs & Diffusion Models | Generate data matching historical distributions | Limited to historical regime structures |
Conditional Diffusion Models (CoDi) | Efficient conditioning on specific inputs [5] | Cannot generate unconditioned novel scenarios |
Self-Rewarding Models | Open-ended scenario generation beyond observed data | Requires careful internal reward engineering |
While CoDi focuses on conditioning within known distributions, SRLMs open the door to exploring the unobserved – creating market scenarios we’ve never seen, but could plausibly occur.
A Vision for the Future
Imagine training an SRLM on decades of market data combined with macroeconomic conditions. The model learns not just patterns, but relationships, dynamics, and implied possibilities. It begins to generate market scenarios that:
🔹 Combine volatility and factor regimes in unseen ways
🔹 Model tail events consistent with historical extremes but in new contexts
🔹 Help risk managers and strategists test portfolio resilience beyond imagination anchored by the past
In a world of increasing structural shifts, generative systems that create plausible futures rather than extrapolations of the past will become a cornerstone of robust financial strategy.
Final Thoughts
Self-rewarding language models represent an emerging frontier in AI – one that moves beyond mimicry towards creative, open-ended generation. For finance, this translates to a new capability: testing the untested and preparing for the unknown.
At Ahead Innovation Labs, we are actively exploring how SRLM frameworks can augment our synthetic market data solutions, empowering quants, risk managers, and systematic traders to build strategies that thrive in futures unbounded by history.
Want to discuss further?
👉 Reach out to explore how self-rewarding models could enhance your trading and risk workflows.
References
[1] Wei Zhe, Yuanzhe Richard Pang, Xian Li, Kyunghyun Cho, Sainbayar Sukhbaatar, Jing Xu. Self-Rewarding Language Models. arXiv preprint arXiv:2401.10020, 2024. Available at: https://arxiv.org/pdf/2401.10020
[2] JP Morgan AI Research. Diffusion Models for Financial Time Series. arXiv preprint arXiv:2307.01717v2, 2023. Available at: https://arxiv.org/pdf/2307.01717v2
[3] Yang et al. Financial Scenario Generation using Conditional GANs. arXiv preprint arXiv:2201.04871, 2022. Available at: https://arxiv.org/pdf/2201.04871.pdf
[4] Wang et al. Generative Market Simulation. arXiv preprint arXiv:2301.09279, 2023. Available at: https://arxiv.org/pdf/2301.09279.pdf
[5] Mikołaj Bińkowski et al. CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image Generation.arXiv preprint arXiv:2310.01407, 2023. Available at: https://arxiv.org/pdf/2310.01407
