Rollout — sampling and π_old

Autoregressive sampling, why we sample K per prompt, and why old_logp has to be captured at sampling time — not recomputed later.

What "rollout" means here

A rollout is the act of producing one response from the policy. Concretely: feed the prompt tokens to the LLM, look at the logits for the next token, sample one, append it, repeat. The full sequence of sampled tokens — usually until an end-of-turn token or a length cap — is one trajectory.

prompt:      <3+4+5>
sample t=0:  '1'              π_θ(·|prompt)       → multinomial → '1'
sample t=1:  '2'              π_θ(·|prompt,'1')   → multinomial → '2'
sample t=2:  '#' (EOS)        π_θ(·|prompt,'12')  → multinomial → '#'
response:    12#

Three observations about that snippet that turn into design pressure on the framework:

1. The forward pass at each step is purely inference. No gradients flow. No optimizer state. We just need next-token logits as fast as possible.
2. The dominant cost is the KV-cache. A naive implementation re-encodes the whole prefix every step, which is O(T²). Production systems cache the keys/values for past tokens and pay O(T) per step. This is what vLLM, SGLang, TensorRT-LLM exist to do well.
3. The reward is terminal. The verifier reads the whole final response. There's no per-token reward to credit-assign with; that responsibility lands on the algorithm in lesson 25.

Why we sample K — variance reduction

Recall the policy gradient: ∇J ≈ r(y) · ∇ log π_θ(y) with y ∼ π_θ. One sample is a high-variance estimate. With K samples per prompt we get K independent estimates of the gradient — which we'll average — and, crucially, we can compute a baseline: the mean reward in the group. Subtracting that mean cuts the variance dramatically without biasing the gradient (because the gradient of a constant times ∇log π is zero in expectation).

Interactive · K rollouts on the toy task

Below: a stand-in policy that has settled on a (deliberately imperfect) habit. Click Sample K trajectories. Each row is one rollout — same prompt, different sampled responses. The reward column is filled by the same verifier you'll meet in lesson 24.

The other thing the rollout records: old_logp

At each sampling step we already compute log-probabilities for the chosen token (we needed them to sample). Storing them costs essentially nothing. Why bother? Because the loss in lesson 25 needs the ratio

where π_θ is the current trainer's policy and π_old is whatever policy actually produced the tokens. If the trainer takes a step between sampling and computing the loss, these two are different policies. Recomputing old_logp after the step gives you π_θ(y) / π_θ(y) = 1 exactly — silently destroying the entire purpose of the ratio.

Where this maps in the code

Inside rl_framework/rollout.py, the generation kernel _sample_from_prompt does both jobs at once:

logits, _ = self.model(ids)              # one forward pass; (K, T, V)
last = logits[:, -1, :] / temp            # (K, V)
logp = F.log_softmax(last, dim=-1)        # (K, V)
probs = logp.exp()                        # (K, V)
nxt   = torch.multinomial(probs, 1).squeeze(-1)   # sample;  (K,)

# This line is the whole reason rollout.py exists rather than reusing
# rl_sota/core.rollout_group: we store the log-prob of the *sampled*
# token, in the policy that produced it. That's π_old.
step_logp = logp.gather(-1, nxt.unsqueeze(-1)).squeeze(-1)   # (K,)

resp_ids [:, t] = nxt
resp_mask[:, t] = (~done).float()
tok_logp [:, t] = step_logp * resp_mask[:, t]

One subtlety: we sample from logp.exp() rather than from a separately-computed softmax. That ensures the sampling distribution and the scored distribution are identical to bit-equality. Sampling from one distribution and scoring under another is a famous bug — temperature scaling applied differently in the two paths is the most common cause.

Why this is its own role

A rollout engine has a completely different shape from a trainer:

In a single process they block each other (you never overlap them — one finishes, the other starts). Splitting rollout into its own role with its own GPUs is the entire economic reason production frameworks like verl, OpenRLHF, and SLIME exist.