ProAct: Agentic Lookahead in Interactive Environments

You know how when you play a long board game, you don’t just move your piece—you look a few turns ahead to avoid traps? That’s what good AI agents need to do in interactive worlds like puzzles, apps, or robots: plan multiple steps into the future.

🍞 Hook: Imagine you’re teaching a puppy new tricks. 🥬 The Concept (Reinforcement Learning): Reinforcement Learning (RL) is a way to teach an agent by rewarding good choices and penalizing bad ones.

• How it works:
  1. The agent tries an action.
  2. The world gives a reward or penalty.
  3. The agent updates its strategy to get more future rewards.
• Why it matters: Without RL, agents just guess and don’t improve from experience. 🍞 Anchor: A robot vacuum learns which paths clean faster by getting “clean room” points and avoiding bump penalties.

🍞 Hook: Picking a line at a theme park. 🥬 The Concept (Value Estimation): Value estimation predicts how good a state or action will be in the future.

• How it works:
  1. Look at the current situation.
  2. Predict future rewards you’ll likely collect.
  3. Prefer choices with higher predicted totals.
• Why it matters: Without value estimation, agents take short-term candy over long-term healthy gains. 🍞 Anchor: Choosing the slightly longer line that you know moves faster overall.

🍞 Hook: Using a map app. 🥬 The Concept (Search Algorithms): Search explores many possible action paths to find good ones.

• How it works: Expand choices step-by-step, score paths, and keep promising branches.
• Why it matters: Without search, you may miss the route that avoids a giant traffic jam. 🍞 Anchor: Trying different turns in a maze on paper before walking it.

🍞 Hook: Sampling ice cream flavors before buying. 🥬 The Concept (Monte-Carlo Tree Search, MCTS): MCTS grows a tree of future possibilities by simulating many random plays and using results to focus on better branches.

• How it works:
  1. Simulate random futures from the current state.
  2. Record scores at the ends.
  3. Prefer branches that pay off more.
• Why it matters: Without MCTS, deep planning becomes guessy, slow, or both. 🍞 Anchor: In 2048, try short sequences like up→left→up to see which move opens merges.

🍞 Hook: Playing chess, you think several moves ahead. 🥬 The Concept (Lookahead Reasoning): Lookahead is mentally simulating possible futures before you act.

• How it works: Imagine outcomes of each action, compare, then pick the safest/best.
• Why it matters: Without lookahead, you blunder into traps you could have seen coming. 🍞 Anchor: In Sokoban, you test whether pushing a box left now blocks the goal later.

🍞 Hook: The telephone-whisper game. 🥬 The Concept (Simulation Drift): Simulation drift is when small prediction mistakes in imagined futures stack up and lead you off-course.

• How it works: Each imagined step is a bit off; many steps become very off.
• Why it matters: Deeper “thinking” can make results worse if it’s ungrounded. 🍞 Anchor: An LLM “imagines” a 2048 merge that the real game would never allow, so its plan fails.

Before this paper, LLM agents used tricks like Chain-of-Thought or ReAct to think aloud and act, and sometimes used search at inference (like Tree-of-Thoughts). But there were big pains:

• Compounding errors: imagined futures drift away from reality the deeper you go.
• Expensive search: running MCTS or big trees at inference is slow and costly.
• Shaky learning: standard critics for multi-turn language actions often estimate value with high variance, making RL updates unstable.

People tried: making the thoughts longer (which often increased drift), running search every time (too slow), or training neural critics (often noisy for long horizons). None cleanly gave “accurate foresight, fast at runtime, and stable to train.”

The gap: a way to teach the agent grounded foresight during training and then act quickly at test time, plus a value signal that’s steady enough to stabilize multi-turn RL.

The real stakes: Better planning AIs can help in everyday apps (scheduling assistants that don’t paint you into a corner), games (smarter but efficient bots), education (tutors planning lessons), and robotics (safer multi-step actions).

The “Aha!” in one sentence: Teach the agent to think ahead using real, ground-truth futures during training (so it learns accurate foresight), then refine it with steady, cheap value feedback so it stays stable when learning to act.

🍞 Hook: Taking messy class notes and making a neat study guide. 🥬 The Concept (Reasoning Compression/Distillation): Distillation turns heavy planning traces into short, clear reasoning the model can use quickly.

• How it works:
  1. Run a strong planner (like MCTS) that explores many futures.
  2. Convert the big search tree into a concise, causal reasoning chain.
  3. Fine-tune the model to use this chain as its inner “sense of the future.”
• Why it matters: Without compression, you’d need slow, expensive search at every step. 🍞 Anchor: Summarizing a big 2048 search into “Up merges two 2’s, keeps corner, then left sets up an 8 next.”

Three analogies for ProAct’s idea:

1. Training wheels: First, you ride with training wheels (environment-grounded search), then you remove them but remember the feel of balance (internalized reasoning).
2. Taste test then recipe: You sample many cooking variations (MCTS) and write a simple recipe that recreates the best flavor fast (compressed reasoning).
3. Weather forecast then habit: You use accurate forecasts (environment rollouts) to form reliable habits (policy), then you need only a glance to decide if you need an umbrella.

Before vs After:

• Before: Agents either imagined futures that drifted from reality or needed heavy search at runtime; RL was unstable on long tasks.
• After: Agents carry an internal, grounded foresight they learned from real futures and get steady, low-variance signals for RL updates, making them both accurate and efficient.

Why it works (intuition):

• Ground truth anchors: When the model sees real outcomes from the environment during training, it learns the true cause-and-effect rules, not imagined ones.
• Compression preserves what matters: Summaries keep the causal links (“If I push here, that box traps.”) without the clutter.
• Monte-Carlo averaging calms the noise: Many quick rollouts give a smoother estimate of “how good this situation is,” so the learning updates don’t wobble.

Building blocks in ProAct:

• GLAD (Grounded LookAhead Distillation): • Use MCTS to explore futures. • Feed those grounded futures to the agent. • Compress the search into short, causal reasoning chains. • Supervised fine-tuning teaches the model this style of accurate foresight.
• MC-Critic (Monte-Carlo Critic): • For a state, run many fast rollouts with a lightweight policy (even random) to estimate future return. • Use this low-variance value as a helper signal in policy-gradient methods like PPO/GRPO. • Result: Stable training that rewards true long-term planning.

🍞 Hook: A coach who watches many scrimmages to judge team strength. 🥬 The Concept (MC-Critic): MC-Critic estimates how good a state/action is by averaging results from many quick environment rollouts.

• How it works:
  1. From a state, run many short, cheap play-outs (often with a random policy).
  2. Average their future scores to estimate value.
  3. Use this estimate to guide the agent’s updates.
• Why it matters: Without MC-Critic, value estimates can swing wildly, making training unstable. 🍞 Anchor: In 2048, quickly simulating many random futures from “move up” vs “move left” reveals which tends to score more next.

🍞 Hook: Planning a move by checking the real board rules, not guesses. 🥬 The Concept (GLAD): GLAD teaches the model to think ahead using futures checked by the real environment and then compresses that into neat reasoning.

• How it works:
  1. Run MCTS to see real, grounded futures (good paths and dead ends).
  2. Prompt the model to analyze these futures.
  3. Compress the analysis into a short chain: Observation → Analysis → Conclusion.
  4. Fine-tune so the model learns to produce such chains itself, without MCTS at test time.
• Why it matters: Without GLAD, the model’s lookahead drifts; with GLAD, it aligns with reality. 🍞 Anchor: In Sokoban, “If I push up now, box A reaches a corner it can’t leave; so push left first, then up.”

At a high level: State text → Stage 1 (GLAD: grounded lookahead + compression + SFT) → Stage 2 (RL with MC-Critic: steady value help) → Better actions and reasoning.

Stage 1: GLAD (Grounded LookAhead Distillation)

1. What happens:

• The environment is the teacher. From the current state, we run MCTS to collect many true futures: some great, some dead ends.
• The model reads these futures and writes an analysis comparing options.
• We then “compress” this into a short, clean reasoning chain: Observation → Analysis (pros/cons, counterfactuals) → Conclusion (chosen action and why not others).
• We fine-tune the model to produce such chains from the state alone.

1. Why this step exists:

• Problem fixed: simulation drift. Ungrounded imagination wanders; grounded futures tether the model to reality. Compression keeps only the causal essence, so the model can think fast later.

1. Example (2048):

• State: Top row 128-8-4-2; a 2 at bottom-right.
• MCTS futures say: Up merges the two 2’s in the right column, opens space, preserves corner; Right pushes small tiles the wrong way.
• Compressed chain: “Up merges 2’s, keeps 128 corner, and sets up left merge; right misaligns. Choose up.”

Cognitive Compression: the secret inside GLAD

• Format simplification: Natural language, no special tags.
• Explicit causal links: “If I do X, Y happens later because of rule Z.”
• Future trend estimation: Not just why the chosen action is good, but why others are risky later.
• Preserve diversity: Reflect trade-offs so the model doesn’t get brittle.

Stage 2: RL with MC-Critic

1. What happens:

• We keep training with RL (PPO or GRPO) so the model maximizes long-term rewards.
• We plug in MC-Critic: from a state (or after a chosen action), we run many quick rollouts with a lightweight policy (often random) to estimate how promising that situation is.
• This averaged estimate is used to compute advantages for policy-gradient updates, reducing variance and stabilizing learning.

1. Why this step exists:

• Problem fixed: high-variance value estimates in multi-turn language actions. Traditional critics on few samples wobble; Monte-Carlo averaging calms the noise.

1. Example (Sokoban):

• At a tricky fork, quick random rollouts show that pushing right leads to deadlocks more often than pushing up. The agent gets a clearer “don’t go right” signal.

How PPO/GRPO are adapted (kid-friendly view):

• PPO/GRPO are like “nudge the policy in the direction that worked better.”
• Step-level variants break long stories into turns, so each decision gets its own feedback.
• With MC-Critic, each turn’s feedback includes a peek into short future returns from many quick trials, not just the immediate reward.
• Extra trick: If a sampled group accidentally tries the same action every time, a special baseline compares that action against all possible actions so learning still happens.

🍞 Hook: Adjusting your aim while shooting hoops. 🥬 The Concept (Policy-Gradient Methods like PPO/GRPO): These adjust the chances of actions that led to better outcomes.

• How it works:
  1. Try actions and collect rewards.
  2. Compare each action to a baseline (advantage).
  3. Increase probability of better-than-baseline actions; decrease for worse.
• Why it matters: Without this, the agent won’t steadily favor what actually works. 🍞 Anchor: If bank shots scored more points for you, you slightly prefer them next time.

Why the recipe is clever (the secret sauce):

• GLAD turns heavy search into light intuition the agent can run fast.
• MC-Critic turns shaky value guesses into steady averages using quick rollouts.
• Together: accurate foresight plus stable learning.

Putting it all together (pipeline):

• Input: Text state from 2048 or Sokoban.
• GLAD: Probe with MCTS → analyze futures → compress reasoning → SFT so the model internalizes it.
• RL+MC-Critic: Keep learning with PPO/GRPO; use many quick rollouts to stabilize the value and reward long-term decisions.
• Output: An agent that explains and chooses better actions quickly.

The tests and why they matter:

• 2048 (stochastic): Tests planning under uncertainty and very long horizons. Score is total points from merges.
• Sokoban (deterministic): Tests precise multi-step planning with sparse rewards. Score is average boxes placed on targets (smooth signal for partial progress).
• Variants: New grid sizes (3×3), changed tile rules (3072), unseen Sokoban levels, changed action spaces, and swapped symbols—to check generalization.

Who ProAct competed against:

• Open-source instruction models (like Qwen3-4B-Instruct) and larger ones.
• Closed-source leaders (e.g., GPT-5, Claude 4.5 Sonnet) for perspective.
• RL baselines: PPO, GRPO, and their multi-turn variants (trajectory-level vs step-level).

The scoreboard with context:

• GLAD alone lifts a 4B model far above open-source baselines on both games and keeps gains on variants. Example on 2048 (4×4): base ~721 vs GLAD ~3335 (huge jump). On Sokoban (Base levels): base ~0.39 boxes vs GLAD ~0.72.
• Adding MC-Critic improves further. Example: 2048 rises to ~4504; Sokoban (Base) to ~0.94. That’s like going from barely passing to top of the class, and doing it consistently across different test versions.
• Training with RL from scratch (no GLAD) still benefits from MC-Critic: MC-PPO and MC-GRPO often beat their counterparts, especially on long-horizon 2048.
• Generalization: Gains hold on 3×3 and 3072 variants in 2048 and on unseen/action/symbol variants in Sokoban; MC-PPO reached up to about 1.18 boxes on unseen Sokoban sets.

Surprising findings:

• More rollouts (M) is not always better for sparse rewards. On Sokoban, very large M averages can wash out the signal (many rollouts score zero), so smaller M can work better.
• Rollout length (T) has a sweet spot. On 2048, going from short to moderate horizons improves performance, but very long T adds variance and can hurt.
• Step-level GRPO (with MC-Critic) is more stable than trajectory-level when episodes are long and noisy (like 2048), because it assigns cleaner credit per decision.
• GLAD compressions that include “why not the other actions” help the model learn counterfactuals and avoid traps.

Plain-language take:

• Stage 1 gives the model the “feel” of correct foresight from real futures.
• Stage 2 polishes that feel with steady, averaged feedback.
• Together, they beat strong baselines and travel well to new, changed environments.

Limitations (honest list):

• Training cost: Building MCTS trees and running many Monte-Carlo rollouts is compute-heavy, even if inference later is cheap.
• Simulator required: You need an environment you can step quickly. Real robots or web tasks without safe simulators are harder.
• Sparse rewards: MC-Critic with a random rollout policy can give flat (near-zero) estimates when success is rare; tuning M and T helps but isn’t magical.
• Compression quality: If search doesn’t explore well, the compressed reasoning can bake in blind spots.
• Text I/O constraints: Everything is serialized as text; format or parsing errors count as invalid moves and can skew learning.

Required resources:

• A fast environment simulator (2048/Sokoban are quick; real-world may not be).
• GPUs for SFT and RL fine-tuning on a 4B model.
• Infrastructure for parallel rollouts (AReaL or similar) to keep training time reasonable.
• Storage for datasets (tens of thousands of steps) and logs.

When not to use ProAct:

• No simulator or environment access (only offline logs with no way to probe futures).
• Physical systems where rollouts are unsafe/expensive (e.g., high-risk robotics) unless you have a high-fidelity sim.
• Ultra-sparse, one-shot tasks with huge action spaces where random rollouts almost never provide signal.
• Pure single-turn QA where lookahead adds little.

Open questions:

• Can we learn a cheap, smarter rollout policy (better than random) that still runs fast and boosts MC-Critic?
• How to auto-tune rollout count (M) and length (T) per task to balance variance vs bias?
• Can compression be made verifiably causal (e.g., include minimal counterfactual proofs)?
• How well does ProAct scale to complex GUIs or web navigation with many actions and partial observability?
• Can we combine GLAD with learned world models to reduce reliance on environment probes while staying grounded?

In three sentences: ProAct teaches LLM agents to look ahead accurately by first learning from real, search-verified futures (GLAD) and then refining with steady, low-noise value estimates from many quick rollouts (MC-Critic). This two-stage approach beats strong baselines on long-horizon games like 2048 and Sokoban, and generalizes robustly to new variants. The result is planner-quality foresight distilled into fast, reliable behavior.

Main achievement: Turning expensive, accurate planning (MCTS) into compact, causal reasoning the model can run quickly—then stabilizing multi-turn RL with a plug-in Monte-Carlo value helper.

Future directions: Smarter surrogate rollouts for MC-Critic, adaptive M/T schedules, compression with stronger counterfactual guarantees, scaling to richer interactive apps (GUIs, web), and combining GLAD with learned world models.

Why remember this: ProAct shows how to bottle “true lookahead” inside an agent so it thinks like a strategist but moves like an expert—accurate, stable, and fast in interactive worlds.