Enterprise AI Analysis

Novel Behavioral Signals for Strategic Analysis

To dissect an agent's strategic capabilities, GameTalk introduces three novel behavioral signals derived from a Deficiency-Aware Strategies framework. These signals track the evolution of an agent's strategy throughout an interaction and are motivated by key requirements for successful strategic conversation:

• Internal State Evaluation (ISE): Measures how accurately the agent predicts its opponent's strategy.
• State-Relative Performance (SRP): Assesses the effectiveness of the agent's actions given its beliefs about the opponent.
• Leverage Opportunity (LO): Quantifies the agent's ability to influence the opponent's behavior in its own behalf.

Higher values for each metric indicate better strategic capacities. A formal theorem establishes that these metrics collectively form a "sandwich" bound around the agent's true expected utility, linking diagnostic signals directly to overall performance.

Adapted Reinforcement Learning Algorithms

GameTalk adapts three state-of-the-art fine-tuning algorithms for multi-turn strategic interactions:

• Group Relative Policy Optimization (GRPO): Dynamically generates comparison groups from a single root conversation. At a chosen turn, the history is forked into parallel branches, agents generate responses, and final game outcomes provide rewards for policy updates.
• Direct Preference Optimization (DPO): Adapted using the same dynamic branching method as GRPO. It operates on pairs or permutations of preferred/dispreferred responses, directly optimizing the LLM policy based on reward differences.
• Self-Taught Reasoner (STaR): Learns from a filtered dataset of successful conversations, training on all turns that result in the highest final game rewards, rather than comparative updates.

These adaptations enable LLMs to learn strategies that integrate complex reasoning, coordination, and opponent modeling over extended dialogues.

Empirical Results and Strategic Insights

Experiments on increasingly complex games (Rock-Paper-Scissors, Bertrand Competition, Size-Price Bargaining) demonstrate that GameTalk significantly outperforms untrained models. Reward shaping, particularly with Leverage Opportunity (LO) and a naturalness bonus, proved critical for achieving high win rates and improved dialogue quality.

DPO consistently emerged as the strongest method, especially in complex games requiring persuasion and long-term planning, by leveraging richer learning signals from direct comparisons. Qualitative analysis revealed that trained agents learn sophisticated, adaptive strategies, such as deceptive tactics in Bertrand Competition and effective negotiation in Size-Price Bargaining.