Quantify the trade‑off between token‑budgeted chain‑of‑thought, uncertainty‑driven budgets, and LLM counterfactual rewards using Bayesian optimisation and Monte‑Carlo simulation.
Bayesian OptimisationMonte Carlo SimulationMulti‑Agent Reinforcement Learning (MARL)LLM‑Driven Counterfactual GenerationFeasibility
Provides quantitative trade‑off curves between budget and performance, informing policy design before costly training runs.
What Is Modelled
A closed‑loop MARL policy that selects token budgets for chain‑of‑thought explanations, adapts uncertainty thresholds, and weights counterfactual rewards to maximise explanation fidelity while minimising sample cost.
Objectives
Determine optimal token‑budget ranges that preserve policy performance within a 5% drop in reward.
Quantify how uncertainty‑driven budget adjustments affect sample efficiency (episodes to convergence).
Assess the impact of LLM‑generated counterfactual reward shaping on explanation fidelity.
Produce a Pareto frontier of token‑budget vs. sample‑efficiency trade‑offs.
Success Criteria
Bayesian optimiser converges to a hyper‑parameter set that yields ≥90% of baseline reward with ≤40% of baseline token usage.
Monte‑Carlo simulation (≥2000 runs) shows statistically significant (p<0.01) improvement in explanation fidelity over random search.
A published parameter‑response surface is produced and validated on a held‑out MARL environment.
All simulation runs finish within the allocated compute budget (≤ 500 GPU‑hours).
Output Form
Parameter‑response surface plots, a CSV of hyper‑parameter settings with associated metrics, and a Python module exposing a `budget_optimizer` API.
Key Parameters & What They Affect
Parameter
Range / Units
Affects
Notes
token_budget
50–200 tokens
explanation fidelitysample cost
Lower budgets reduce latency but may truncate reasoning steps.
uncertainty_threshold
0.1–0.5 (entropy units)
sample efficiencypolicy robustness
Higher thresholds trigger longer explanations when observation uncertainty is high.
counterfactual_weight
0.0–1.0
explanation fidelitypolicy exploration
Weight applied to counterfactual reward shaping in the MARL loss.
exploration_noise
0.0–0.3
sample efficiencypolicy robustness
Standard deviation of Gaussian exploration noise added to actions.
Input Data
Required data:
Multi‑agent interaction logs from AOI‑GBE (natural source)
LLM prompt templates for counterfactual generation (natural source)
Baseline MARL reward curves from SMAC or StarCraft Multi‑Agent Challenge (acquired source)
Synthetic observation noise samples generated by CC‑GAN (synthesised source)
Natural Sources (from the project)
Chapter 1 AOI‑GBE interaction logs (see roadmap chapter 01)
Chapter 3 LLM‑AC curriculum outputs (see roadmap chapter 03)
Chapter 4 explanation fidelity metrics from prior experiments (see roadmap chapter 04)
HuggingFace Llama‑3 LLM API (https://huggingface.co/models)
Synthesised Sources
CC‑GAN trained on nominal + adversarial sensor streams to produce synthetic perturbed observations
Monte‑Carlo roll‑outs of MARL agents with varied hyper‑parameters to generate simulation data
Engineer / Scientist Guidance
Set up a reproducible environment: create a Conda environment with PyTorch 2.1, Ray 2.10, and Ax 0.12.
Load the AOI‑GBE logs and extract observation‑policy pairs; use these to initialise the CC‑GAN generator for synthetic noise.
Implement the MARL agent using MADDPG or QMIX; expose hyper‑parameters (token_budget, uncertainty_threshold, counterfactual_weight, exploration_noise) as tunable knobs.
Wrap the agent in a Ray Tune training loop; define a custom `BudgetTrial` that records reward, token usage, and explanation fidelity per episode.
Configure Ax to use a Gaussian Process with Expected Improvement acquisition; set the search space to the key parameters listed above.
Run 2000 Monte‑Carlo trials, each with 500 episodes; store results in a Parquet file for downstream analysis.
Post‑process the Ax results to extract the Pareto frontier; plot token_budget vs. reward and token_budget vs. explanation fidelity.
Validate the best hyper‑parameter set on a held‑out SMAC map; confirm that reward loss <5% and token usage <40% of baseline.
Package the final hyper‑parameter configuration into a `budget_optimizer.py` module with a `predict_budget(state)` function.
Document the entire pipeline in a Jupyter notebook and commit to the project repo.
The simulation will be validated by (1) comparing the reward curve of the tuned agent against a baseline agent with no explanation budget; (2) computing the explanation fidelity score using a pre‑defined metric (e.g., cosine similarity of token embeddings to a gold explanation set); (3) performing a statistical test (Wilcoxon signed‑rank) on 100 held‑out episodes to confirm significance; and (4) cross‑validating on a second MARL environment (e.g., StarCraft II micromanagement).
Expected Impact
Quality
Provides a quantitative mapping between explanation budget and policy performance, enabling designers to set token limits that satisfy regulatory transparency without sacrificing safety.
Timescale
Reduces the need for ad‑hoc tuning by 30% by automating hyper‑parameter search.
Cost
Limits simulation runs to 2000 Monte‑Carlo episodes, cutting compute spend by ~40% compared to exhaustive grid search.
Risk Retired
Mitigates the risk of over‑explanation (token waste) and under‑explanation (hallucination) that could lead to regulatory non‑compliance.
Software Tool Development Prompts
Drop these into a coding assistant toscaffold the supporting software for this modelling task.
Create a Python script that sets up an Ax Bayesian optimisation study for the following hyper‑parameters: token_budget (50-200), uncertainty_threshold (0.1-0.5), counterfactual_weight (0.0-1.0), exploration_noise (0.0-0.3). The study should run 2000 trials, each invoking a Ray Tune training job that returns reward, token_usage, and explanation_fidelity. Store the results in a Parquet file and plot the Pareto frontier using Plotly. Include comments explaining each step.
Write a PyTorch module `CounterfactualReward` that takes a batch of states and actions, generates counterfactuals using a HuggingFace Llama‑3 pipeline, and returns a reward shaping term weighted by a tunable `counterfactual_weight`. Ensure the module runs on GPU and can be integrated into a MADDPG loss function.
Risks & Assumptions
Assumption: The Llama‑3 LLM can generate counterfactuals within the token budget; if not, fallback to a smaller model.
Risk: The Gaussian Process surrogate may over‑fit to noisy reward signals; mitigated by adding jitter and using a robust kernel.
Risk: Synthetic observation noise from CC‑GAN may not capture real adversarial distributions; mitigated by mixing with real AOI‑GBE logs.
Risk: Ray Tune cluster may run out of GPU memory when training many agents; mitigated by limiting concurrent trials to 4.
Assumption: Explanation fidelity metric correlates with human interpretability; future work will involve human‑in‑the‑loop validation.