Reinforcement Learning

Reinforcement Learning (RL) has become a seductive tool for economists seeking to simulate adaptive behavior in dynamic, uncertain environments. But when it comes to modeling firms in equilibrium labor markets, this computational marriage reveals some serious incompatibilities. In a recent paper, Zhang and Chen expose two critical mismatches that emerge when standard RL is naively applied to simulate economic models — and offer a principled fix that merges the best of RL and economic theory. ...

In an age where generative models can ace SATs, write novels, and mimic empathy, it’s no longer enough to ask, “Can an AI fool us?” The better question is: Can we still detect it when it does? That’s the premise behind the Dual Turing Test, a sharp reframing of the classic imitation game. Rather than rewarding AI for successfully pretending to be human, this framework challenges judges to reliably detect AI—even when its responses meet strict quality standards. ...

When a large language model (LLM) answers your question with a high degree of confidence, do you trust it? What if it’s wrong—but still confident? The stakes are high in real-world applications, from legal guidance to enterprise decision support. Yet today’s LLMs remain notoriously unreliable in aligning their confidence with correctness. The paper Deliberative Searcher: Improving LLM Reliability via Reinforcement Learning with Constraints (Yin et al., 2025) offers a bold response: rewire LLMs to be reasoning-primary and information-secondary. Instead of front-loading search and passively absorbing evidence, Deliberative Searcher acts more like a prudent investigator: it thinks, self-assesses, retrieves external information only when needed, and calibrates its confidence step-by-step. Crucially, it learns this behavior through a custom constrained reinforcement learning regime. ...

What if you could simulate thousands of realistic futures for the market, all conditioned on what’s happening today—and then train an investment strategy on those futures? That’s the central idea behind a bold new approach to portfolio optimization that blends score-based diffusion models with reinforcement learning, and it’s showing results that beat classic benchmarks like the S&P 500 and traditional Markowitz portfolios. ...

In the arms race to align large language models (LLMs), supervised fine-tuning (SFT) and reinforcement learning (RL) are often painted as competing paradigms. SFT is praised for its stability and simplicity; RL is heralded for its theoretical soundness and alignment fidelity. But what if this dichotomy is an illusion? A recent preprint from Chongli Qin and Jost Tobias Springenberg makes a bold and elegant claim: SFT on curated data is not merely supervised learning—it is actually optimizing a lower bound on the RL objective. ...

In the race to make Large Language Models (LLMs) reason like humans—or better—most researchers obsess over one thing: prompting. Chain-of-thoughts, few-shot demos, scratchpads, tools. But a new study from NVIDIA suggests something even more fundamental: it’s not just how you prompt them—it’s how long you train them. Their paper, Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training, explores how stretching reinforcement learning (RL) over time unlocks broader, more stable, and more versatile reasoning in LLMs. This isn’t just about incremental gains—it’s about escaping reasoning ruts. ...

Reinforcement learning (RL) has recently emerged as the favored path to boost large language models’ reasoning abilities. The latest headline-grabbing claim? That even random or incorrect reward signals can help models like Qwen2.5 become better reasoners. But a new paper, “Reasoning or Memorization?”, cuts through the hype—and it does so with scalpel-like precision. It reveals that what we thought were signs of emergent reasoning in Qwen2.5 might, in fact, be a textbook case of data contamination. If true, the implications are serious: much of what we thought we knew about RL-driven reasoning gains could be little more than sophisticated memory retrieval. ...

If GPT-4 was the apex of pretraining, DeepSeek might be the blueprint for what comes next. Released in two families—DeepSeek-V3 and DeepSeek-R1—this Chinese open-source model series isn’t just catching up to frontier LLMs. It’s reshaping the paradigm entirely. By sidestepping traditional supervised fine-tuning in favor of reinforcement learning (RL), and coupling it with memory-efficient innovations like Multi-head Latent Attention (MLA) and cost-efficient training techniques like FP8 mixed precision and fine-grained MoE, DeepSeek models demonstrate how strategic architectural bets can outpace brute-force scale. ...

A persistent mystery in the recent surge of reasoning-augmented LLMs—like OpenAI’s o1 or DeepSeek-R1—is whether these models learn to reason through post hoc reinforcement fine-tuning, or if they were already good at it to begin with. ASTRO offers a rare counter-example: a method that imbues non-reasoner LLMs (like vanilla Llama 3) with structured reasoning behavior from scratch. Rather than rely on emergent capabilities or distillation from models that already search well, ASTRO teaches LLMs to think like search algorithms themselves, using a hybrid approach combining Monte Carlo Tree Search (MCTS), procedure cloning, chain-of-thought generation, and reinforcement learning with verifiable rewards. ...

When language models take flight, consensus becomes not just possible, but programmable. Modern UAV swarms face the daunting task of coordinating across partial observability, adversarial threats, and shifting missions. Traditional Multi-Agent Reinforcement Learning (MARL) offers adaptability, but falters when role differentiation or semantic reasoning is required. Large Language Models (LLMs), meanwhile, understand tasks and intent—but lack grounded, online learning. RALLY (Role-Adaptive LLM-Driven Yoked Navigation) is the first framework to successfully integrate these two paradigms, enabling real-time, role-aware collaboration in UAV swarms. ...