Improving Visual Reasoning Through Scalable Training

# Illustrative sketch (not from the paper)
import torch
import random

# 1. Procedural task generator (placeholder for 300 tasks)
def sample_task():
    # returns a dict with task_id and modality ('image' or 'video')
    return {"task_id": random.randint(0, 299), "modality": random.choice(["image", "video"])}

# 2. Simple reward scorer operating on semantic attributes (rule‑based)
def reward_scorer(output, target_attrs):
    # compare semantic attributes, ignore raw pixels
    return 1.0 if output == target_attrs else 0.0

# 3. Coefficients‑Preserving Sampling (CPS) stub for stochastic exploration
def cps_sampling(logits, coeffs):
    # preserve coefficient scaling while sampling
    probs = torch.softmax(logits * coeffs, dim=-1)
    return torch.multinomial(probs, num_samples=1)

# 4. Multi‑task RL training loop (very high‑level)
model = torch.nn.Linear(128, 10)  # placeholder visual generator
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
coeffs = torch.ones(10)  # coefficient vector preserved across tasks
for step in range(1000):
    task = sample_task()
    # generate visual output (placeholder tensor)
    logits = model(torch.randn(1, 128))
    action = cps_sampling(logits, coeffs)
    # obtain semantic target attributes from task definition (mock)
    target = random.randint(0, 9)
    reward = reward_scorer(action.item(), target)
    loss = -torch.log(torch.tensor(reward + 1e-6))  # maximize reward
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

// Illustrative sketch (not from the paper)
const tf = require('@tensorflow/tfjs-node');

// 1. Procedural task sampler (placeholder for 300 tasks)
function sampleTask() {
  return {
    taskId: Math.floor(Math.random() * 300),
    modality: Math.random() < 0.5 ? 'image' : 'video'
  };
}

// 2. Rule‑based reward scorer on semantic attributes
function rewardScorer(output, targetAttr) {
  return output === targetAttr ? 1.0 : 0.0;
}

// 3. CPS‑style sampling preserving coefficient scaling
function cpsSampling(logits, coeffs) {
  const scaled = tf.mul(logits, coeffs);
  const probs = tf.softmax(scaled);
  const sample = tf.multinomial(probs, 1);
  return sample;
}

// 4. Minimal multi‑task RL loop
const model = tf.layers.dense({units: 10, inputShape: [128]}); // placeholder generator
const optimizer = tf.train.adam(1e-4);
let coeffs = tf.ones([10]); // coefficient vector
async function train(steps) {
  for (let step = 0; step < steps; step++) {
    const task = sampleTask();
    const input = tf.randomNormal([1, 128]);
    const logits = model.apply(input);
    const actionTensor = cpsSampling(logits, coeffs);
    const action = (await actionTensor.data())[0];
    const target = Math.floor(Math.random() * 10);
    const reward = rewardScorer(action, target);
    const loss = tf.neg(tf.log(tf.scalar(reward + 1e-6)));
    optimizer.minimize(() => loss);
  }
}
train(1000);