Reinforcement Learning

Opening — Why this matters now Neurosymbolic AI has long promised a synthesis: neural networks that learn, and logical systems that reason. But in practice, the two halves have been perpetually out of sync — neural systems scale but don’t explain, while symbolic systems explain but don’t scale. The recent paper DeepProofLog: Efficient Proving in Deep Stochastic Logic Programs takes a decisive step toward resolving this standoff by reframing reasoning itself as a policy optimization problem. In short, it teaches logic to think like a reinforcement learner. ...

Opening — Why this matters now The AI world has become obsessed with “long-horizon” reasoning—the ability for agents to sustain coherent thought over hundreds or even thousands of interactions. Yet most large language model (LLM) agents, despite their size, collapse under their own memory. The context window fills, noise piles up, and coherence suffocates. Alibaba’s IterResearch tackles this problem not by extending memory—but by redesigning it. ...

Opening — Why this matters now Collaboration is the final frontier of autonomy. As AI agents move from single-task environments to shared, unpredictable ones — driving, logistics, even disaster response — the question is no longer can they act, but can they cooperate? Most reinforcement learning (RL) systems still behave like lone wolves: excellent at optimization, terrible at teamwork. The recent paper PADiff: Predictive and Adaptive Diffusion Policies for Ad Hoc Teamwork proposes a striking alternative — a diffusion-based framework where agents learn not just to act, but to anticipate and adapt, even alongside teammates they’ve never met. ...

Opening — Why this matters now AI agents can code, search, analyze data, and even plan holidays. But when the clock starts ticking, they often stumble. The latest benchmark from Shanghai Jiao Tong University — TPS-Bench (Tool Planning and Scheduling Benchmark) — measures whether large language model (LLM) agents can not only choose the right tools, but also use them efficiently in multi-step, real-world scenarios. The results? Let’s just say most of our AI “assistants” are better at thinking than managing their calendars. ...

Opening — Why this matters now The AI industry has a curious paradox: we can train models to reason at Olympiad level, but they still fumble at booking flights or handling a spreadsheet. The problem isn’t intelligence—it’s context. Agents are trained in narrow sandboxes that don’t scale, breaking the moment the environment changes. Microsoft and the University of Washington’s Simia framework tackles this bottleneck with a provocative idea: what if the agent could simulate its own world? ...

Opening — Why this matters now The line between algorithmic trading and artificial intelligence is dissolving. What once were rigid, rules-based systems executing trades on predefined indicators are now evolving into learning entities — autonomous agents capable of adapting, negotiating, and even competing in simulated markets. The research paper under review explores this frontier, where multi-agent reinforcement learning (MARL) meets financial markets — a domain notorious for non-stationarity, strategic interaction, and limited data transparency. ...

The dream of self-improving intelligence has long haunted AI research—a model that learns not from humans, but from itself. Multi-Agent Evolve (MAE) by Yixing Chen et al. (UIUC, NVIDIA, PKU) gives that dream a concrete architecture: three versions of the same LLM—Proposer, Solver, and Judge—locked in a continuous loop of challenge, response, and evaluation. No human labels. No external verifiers. Just the model, teaching itself through the friction of disagreement. ...

In the fast-evolving landscape of agentic AI, one critical limitation persists: most frameworks can think or act, but rarely both in a fluid, self-directed manner. They follow rigid ReAct-like loops—plan, call, observe—resembling a robot that obeys instructions without ever truly reflecting on its strategy. The recent paper “DeepAgent: A General Reasoning Agent with Scalable Toolsets” from Renmin University and Xiaohongshu proposes an ambitious leap beyond this boundary. It envisions an agent that thinks deeply, acts freely, and remembers wisely. ...

TL;DR Most LLMs reason token‑by‑token and get lost in the weeds. PTA‑GRPO is a two‑stage method that (1) distills short, high‑level plans from a stronger teacher and (2) reinforces both the final answer and the plan’s quality. Across math benchmarks, it reliably outperforms GRPO/DAPO while producing shorter, cleaner solutions. For AI builders, the principle is simple: force an outline, then reward it. Why this paper matters for builders (not just benchmark chasers) From local greed to global guidance. Traditional CoT is myopic: it optimizes each next token. PTA‑GRPO adds a global outline that trims detours and reduces reasoning drift. Aligns with how teams actually work. Great analysts draft an outline before the memo; great agents should too. PTA‑GRPO operationalizes that habit. Product leverage: If your agents make multi‑step decisions (pricing, triage, troubleshooting), rewarding plan quality prevents hallucinated subgoals and makes reasoning auditable. Compute sanity: Instead of expensive tree search at inference, PTA‑GRPO trains planning skill so you can keep runtime simple. The core idea in one picture (words) Plan → Think → Answer. ...

TL;DR SFT memorizes co-occurrences; RL explores. That’s why RL generalizes better on planning tasks. Policy-gradient (PG) can hit 100% training accuracy while silently killing output diversity. KL helps—but caps gains. Q-learning with process rewards preserves diversity and works off‑policy. With outcome‑only rewards, it reward-hacks and collapses. Why this paper matters to builders If you’re shipping agentic features—tool use chains, workflow orchestration, or multi-step retrieval—you’re already relying on planning. The paper models planning as path-finding on a graph and derives learning dynamics for SFT vs RL variants. The results give a crisp blueprint for product choices: which objective to use, when to add KL, and how to avoid brittle one-path agents. ...