Planning

Opening — Why this matters now The AI industry has grown comfortable with a flattering assumption: if a model can reason, it can plan. Multi-step logic, chain-of-thought traces, and ever-longer context windows have encouraged the belief that we are edging toward systems capable of sustained, goal-directed action. SokoBench quietly dismantles that assumption. By stripping planning down to its bare minimum, the paper reveals an uncomfortable truth: today’s large reasoning models fail not because problems are complex—but because they are long. ...

Opening — Why this matters now Robotics has quietly entered an awkward phase. Models can see remarkably well and talk impressively about tasks—but when it comes to executing long-horizon, high-precision actions in the physical world, performance still collapses in the details. Grasp slips. Motions jitter. Multimodal uncertainty wins. At the same time, video generation models have undergone a renaissance. Large diffusion-based video models now encode temporal causality, implicit physics, and motion continuity at a scale robotics has never had access to. The obvious question follows: ...

Opening — Why this matters now Agentic large language models are increasingly marketed as generalist planners: systems that can reason, act, and adapt across domains without bespoke algorithmic scaffolding. The pitch is seductive—why maintain a zoo of solvers when a single agent can plan everything from code refactors to satellite schedules? AstroReason-Bench arrives as a cold shower. ...

Opening — Why this matters now GUI agents are everywhere in demos and nowhere in production. They click, scroll, and type impressively—right up until the task requires foresight. The moment an interface branches, refreshes, or hides its intent behind two more screens, today’s agents revert to trial-and-error behavior. The core problem isn’t vision. It’s imagination. ...

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. ...

TL;DR Most reflection frameworks still treat failure analysis as an afterthought. SAMULE reframes it as the core curriculum: synthesize reflections at micro (single trajectory), meso (intra‑task error taxonomy), and macro (inter‑task error clusters) levels, then fine‑tune a compact retrospective model that generates targeted reflections at inference. It outperforms prompt‑only baselines and RL‑heavy approaches on TravelPlanner, NATURAL PLAN, and Tau‑Bench. The strategic lesson for builders: design your error system first; the agent will follow. ...

When assistants coordinate multiple tools or agents, the biggest unforced error is planning off the raw chat log. RECAP (REwriting Conversations for Agent Planning) argues—and empirically shows—that a slim “intent rewriter” sitting between the dialogue and the planner yields better, cleaner plans, especially in the messy realities of ambiguity, intent drift, and mixed goals. The headline: rewriting the conversation into a concise, up‑to‑date intent beats throwing the whole transcript at your planner. ...

Modern retail planning isn’t a spreadsheet; it’s a loop. A new supply‑chain agent framework—deployed at JD.com’s scale—treats planning as a closed‑loop system: gather data → generate plans → execute → diagnose → correct → repeat. That shift from “one‑and‑done forecasting” to continuous replanning is the core idea worth copying. What’s actually new here Agentic decomposition around business intents. Instead of dumping a vague prompt into a model, the system classifies the operator’s request into three intent families: (1) inventory turnover & diagnostics, (2) in‑stock monitoring, (3) sales/inventory/procurement recommendations. Each intent triggers a structured task list rather than ad‑hoc code. Atomic analytics, not monoliths. The execution agent generates workflows as chains of four primitives—Filter → Transform → Groupby → Sort—and stitches them with function calls to vetted business logic. This keeps code inspectable, traceable, and reusable. Dynamic reconfiguration. After every sub‑task, observations feed back into the planner, which prunes, reorders, or adds steps. The output isn’t a static report; it’s a plan that learns while it runs. Why it matters for operators (not just researchers) Traditional MIP‑heavy or rule‑based planning works well when the world is stationary and well‑specified. Retail isn’t. Promotions, seasonality, logistics bottlenecks, supplier constraints—these create moving objective functions. The agentic design here bakes in: ...

When do you really need a plan? In agentic AI, the answer isn’t “always” (ReAct‑style reasoning at every step) or “never” (greedy next‑action). It’s sometimes—and knowing when is the whole game. A new paper shows that agents that learn to allocate test‑time compute dynamically—planning only when the expected benefit outweighs the cost—beat both extremes on long‑horizon tasks. Why this matters for operators Most enterprise deployments of LLM agents are killed by one of two problems: ...