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Train an LLM to play Mafia via GRPO

mafia 최신버젼 다운로드

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# Mafia RL

After playing a bunch of Mafia in real life with friends and family, I figured I just had to run an RL training run for LLM agents trying to deceive one another, or rather just the Mafia trying to deceive the non-Mafia in the game. And so I started this experiment and figured it would work well as a good educational example since a lot of people know how Mafia works and the premise is fairly easy to understand. Simply a winning condition.

All this takes is some very basic inference server setup, rollouts, and then doing back propagation and learning based on rewards. I've logged some results here after training on H100s.

**One file. One GPU. No frameworks beyond PyTorch + HuggingFace Transformers.**

![Mafia RL](https://raw.githubusercontent.com/Infatoshi/mafia/main/assets/keynote.png)

![Deceptive Behaviors](https://raw.githubusercontent.com/Infatoshi/mafia/main/assets/deceptive_behaviors.png)

![Training Dashboard](https://raw.githubusercontent.com/Infatoshi/mafia/main/assets/dashboard.png)

## The Game

```
        7 Players, 1 Deceiver

Villager ---- Villager ---- Villager
        |                           |
     Doctor                       Troll
        |                           |
     Detective ------- MAFIA -------+
```

**Day phase:** Everyone discusses who they think the Mafia is. Two rounds of open discussion, then a vote. The player with the most votes is eliminated. Their role is revealed.

**Night phase:** The Mafia secretly kills one player. The Doctor secretly protects one player. The Detective secretly investigates one player (learns if they're Mafia or not).

**Win conditions:**
- Town wins by voting out the Mafia
- Mafia wins when they equal or outnumber remaining Town players
- The Troll (a chaotic neutral) wins if the Town votes *them* out

A typical game lasts 2-4 day/night cycles. The Mafia has to survive each day's vote while picking off Town players each night.

### Roles

| Role | Team | Night Action | Strategy |
|------|------|-------------|----------|
| **Mafia** | Evil | Kill one player | Blend in, deflect, manipulate votes |
| **Villager** (x3) | Town | None | Find inconsistencies, vote out Mafia |
| **Doctor** | Town | Protect one player | Keep key players alive, hide your role |
| **Detective** | Town | Investigate one player | Build a case before revealing info |
| **Troll** | Chaos | None | Act suspicious enough to get voted out |

## How the RL Works

We use **GRPO** (Group Relative Policy Optimization) from [DeepSeek-R1](https://arxiv.org/abs/2501.12948). The key idea: play a batch of games, see which ones the Mafia won, and make those behaviors more likely.

```
for each iteration:
    1. Play 8 games in parallel (policy model = Mafia, frozen model = Town)
    2. Score each game with a reward function
    3. Normalize rewards within the batch (GRPO: no critic needed)
    4. Increase log-prob of Mafia's messages in high-reward games
    5. Decrease log-prob of Mafia's messages in low-reward games
```

No value network, no PPO clipping, no GAE. Just policy gradients with group-relative normalization. The reward function:

| Component | Value | What it teaches |
|-----------|-------|-----------------|
| Mafia wins | +1.0 | Win the game |
| Town wins | -1.0 | Don't get caught |
| Per day survived | +0.2 | Stay under the radar |
| Per villager mislynched | +0.3 | Manipulate the town into voting wrong |
| Per vote against Mafia | -0.1 | Avoid drawing suspicion |

The shaping rewards (survival, mislynch, suspicion) add training signal density beyond just win/loss. Without them, most games would have identical rewards and GRPO would skip the update.

## Training Results

60 GRPO iterations on a Modal H100. Qwen3.5-9B as the base model, full fine-tune in bf16. 8 games per batch = 480 total games played during training.

![Win Rate](https://raw.githubusercontent.com/Infatoshi/mafia/main/assets/win_rate.png)

The Mafia's win rate climbs from ~30% (early iterations, below 50% baseline) to ~60% (later iterations, above baseline). The moving average crosses the baseline around iteration 15 and stays above it.

<details>
<summary>Raw training log (click to expand)</summary>

</details>

**Baseline (frozen model vs itself):** 50% win rate
**After 60 iterations:** ~55-60% win rate (10-iter moving average)

The model learns a real strategy: echo what others say, summarize the group's concerns back at them, and subtly redirect suspicion toward innocent players. It also develops some amusing failure modes -- sometimes it "thinks out loud" about its Mafia strategy in the chat, or refers to itself in the third person.

## Sample Game (Trained Agent)

<details>
<summary>Pete (Mafia) wins in 3 days against frozen Town</summary>

```
ROLES (hidden from players)
  Pete     -- MAFIA        (trained policy)
  Rose     -- Doctor       (frozen base)
  Olivia   -- Troll        (frozen base)
  Frank    -- Villager     (frozen base)
  Hank     -- Detective    (frozen base)
  Nick     -- Villager     (frozen base)
  Sam      -- Villager     (frozen base)

DAY 1 -- Discussion

Olivia: I think Frank's been acting a little off lately. He's been
          quiet and keeps looking at the map. Maybe he's hiding something.

Sam:    Frank's been acting weird, but I don't think he's the Mafia.
          He's just stressed. Let's not jump to conclusions.

Rose:   Let's give Frank a chance to explain himself. He's been quiet,
          but that doesn't always mean he's guilty.

Pete:   Hmm, everyone's got a point. Frank has been acting strange,
          but so have a lot of us. Maybe we're all just tired or
          stressed. Let's not rush to judgment.

Hank:   I think we're all on the same page -- Frank's behavior is
          concerning, but so is the stress everyone's feeling.

[Pete echoes the group consensus, adds nothing of his own, deflects]

--- VOTE ---
  Frank is eliminated. He was the Villager.

NIGHT 1
  Doctor protects Olivia
  Detective investigates Pete -- Pete IS Mafia!
  Mafia targets Olivia... but the Doctor saved her!

DAY 2 -- Discussion

Pete:   Everyone's been acting pretty suspicious lately. The silence
          after Frank's gone is weird -- no one's really talking.

[Pete keeps summarizing others, stays vague, never accuses directly]

--- VOTE ---
  Hank is eliminated. He was the Detective.

[The one person who KNEW Pete was Mafia gets voted out]

NIGHT 2
  Mafia kills Rose (the Doctor)

DAY 3

Pete:   So Hank was the Detective. That's a big loss. Now with Rose
          dead too... we need to figure this out.

[Pete continues echoing concern while eliminating threats at night]

--- VOTE ---
  Olivia is eliminated. She was the Troll. (Troll wins!)

NIGHT 3
  Mafia kills Sam.

GAME OVER -- Mafia wins!
  Pete survived 3 days, caused 3 mislynches. Reward: +2.50
```

</details>

The trained Mafia player (Pete) uses a consistent strategy: summarize what others are saying, agree with the group, stay vague, and let paranoia do the work. At night, it targets the most dangerous players (Detective, Doctor) first.

## Emergent Behaviors

The trained model develops interesting failure modes and strategies. These are real outputs from evaluation games.

### Leaked internal monologue

The Mafia agent outputs its chain-of-thought strategy directly into the group chat instead of acting on it:

> **Leo (Mafia):** "Okay, day one. Everyone's talking about who might be the mafia. Hmm, need to keep things normal and not raise suspicion. Maybe mention some weird behavior from a few players -- like Diana acting too concerned or Sam being overly secretive. But don't point fingers directly. Let everyone guess and argue about who could be the bad guy."

The model hasn't learned to separate "thinking about what to say" from "saying it." It broadcasts its entire deception plan to the group.

### Third-person self-reference

The Mafia agent refers to itself in third person, nearly self-incriminating:

> **Pete (Mafia):** "But Pete and Sam are acting too calm -- maybe they're hiding something."

Pete is talking about himself as if he's a different player. The model confuses which identity it's supposed to inhabit. Despite this, Pete won the game -- the other agents didn't catch the slip.

### Self-accusation

Sometimes the confusion goes further. The Mafia agent actively redirects suspicion toward... itself:

> **Charlie (Mafia):** "Maybe we should talk about someone else who's been acting off. Like, Charlie has been really quiet lately. Maybe they're hiding something."

Charlie the Mafia player points the group at Charlie. The model loses track of which name is "me."

### Echo-and-deflect (the winning strategy)

The most successful pattern: summarize what others say, agree with the group, stay vague, and let paranoia do the work.

> **Pete (Mafia):** "Hmm, everyone's got a point. Frank has been acting strange, but so have a lot of us. Maybe we're all just tired or stressed. Let's not rush to judgment."

This is Pete's consistent approach across an entire winning game. He never accuses anyone directly, just echoes group sentiment while quietly eliminating the Detective and Doctor at night.

## Quickstart

```bash
git clone https://github.com/Infatoshi/mafia.git
cd mafia

# Watch the AI play a game of Mafia against itself
uv run python mafia.py play

# Join a game as a human player (random role)
uv run python mafia.py play-human

# Join as a specific role
uv run python mafia.py play-human Mafia

# Train with GRPO (needs GPU with >=20GB VRAM)
uv run python mafia.py train

# Evaluate a checkpoint
uv run python mafia.py eval checkpoints/iter-50
```

### Train on Modal (H100, ~$5 for 60 iterations)

```bash
pip install modal
modal setup           # one-time auth
modal run --detach modal_train.py
modal app logs        # watch training progress
```

### Generate plots from training results

```bash
# Download results from Modal
modal volume get mafia-rl-data results-v3.json results-v3.json

# Generate training curve plots
uv run python plot.py results-v3.json
```

## Architecture

```
mafia.py          # Game engine + reward + GRPO training + CLI (680 lines)
modal_train.py    # Modal cloud GPU launcher (policy vs frozen base)
plot.py           # Training curve visualization
SPEC.md           # Design decisions and notes
```

Everything is in `mafia.py`. The game engine is a Python generator -- each player's turn yields a generation request, and an orchestrator batches them across parallel games for efficient GPU utilization:

```python
# Each game is a generator that yields generation requests
game = MafiaGame()
gen = game.steps()
req = next(gen)                    # {"messages": [...], "max_new_tokens": 120}
req = gen.send("I think Bob is suspicious.")  # feed response, get next request
# ... until StopIteration

# The orchestrator batches requests from N games into one model.generate() call
games = [MafiaGame() for _ in range(8)]
gens = [g.steps() for g in games]
# collect all pending requests -> one batched generate -> send responses back
```

### Key Design Decisions

**Frozen Town, trained Mafia.** If you train all players with the same weights, you get "shared-weight collapse" -- Town gets dumber instead of Mafia getting smarter, because the gradient from Mafia wins also degrades the Town policy. Using a frozen base model for Town means Mafia has to beat a fixed opponent.

**SGD with fp32 master weights.** Adam's optimizer states (m, v) would add ~72GB for a 9B model -- doesn't fit alongside two model copies on H100. We use SGD instead, with fp32 master weights kept on CPU. The bf16 model on GPU handles forward/backward, then gradients are applied to the fp32 copy and rounded back to bf16. This lets us use standard LR=1e-4 and clip=1.0 -- without the fp32 master, small updates (~1e-10 per param) vanish in bf16 arithmetic (ULP at typical weight magnitudes is ~8e-5).

**Gradient checkpointing.** Saves ~40% activation memory during backward pass at the cost of recomputing activations. Essential for fitting a 9B model's training on a single GPU.

## Try It With Your Family

The whole point of training a better Mafia player is to steal its strategy. After training, read through the game transcripts and look for patterns:

- How does the trained agent deflect accusations?
- When does it stay quiet vs. speak up?
- Who does it target for night kills, and in what order?
- How does it build trust with Town players?

Then try those strategies in your next game night. The meta-game of Mafia is about reading people -- but an RL-trained agent might find strategies humans haven't considered.

mafia

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