Principle:OpenRLHF OpenRLHF Interactive Model Inference

Knowledge Sources	OpenRLHF
Domains	Inference, Generation, Evaluation
Last Updated	2026-02-07 10:40 GMT

Overview

Interactive text generation paradigm for evaluating trained language models through a conversational REPL interface.

Description

Interactive model inference provides a read-eval-print loop for real-time text generation from a trained language model. The principle supports both single-turn (template-based prompt formatting) and multi-turn (chat template with conversation history) generation modes. It enables direct evaluation of model quality after training, supporting various decoding strategies (greedy, nucleus sampling, temperature scaling) and quantization options (QLoRA 4-bit) for resource-constrained environments.

Usage

Use interactive model inference for qualitative evaluation of model outputs after SFT, DPO, KTO, or RLHF training. It is a debugging and evaluation tool that complements quantitative metrics by allowing direct observation of generation behavior with arbitrary prompts.

Theoretical Basis

The generation process follows autoregressive decoding:

$P(y_t|y_{<t},x)=\text{softmax}(f_{\theta }(y_{<t},x))$

With configurable sampling strategies:

• Greedy: $y_t=\arg \max P(y_t|y_{<t},x)$
• Nucleus (top-p): Sample from minimal set $V_p$ where $\sum _{y\in V_p}P(y)\ge p$
• Temperature: $P^{\prime }(y_t)=\text{softmax}(f_{\theta }/T)$

Pseudo-code Logic:

# Abstract interactive loop (NOT actual implementation)
model = load_model(checkpoint)
history = []

while True:
    user_input = read_input()
    if chat_mode:
        history.append({"role": "user", "content": user_input})
        prompt = apply_chat_template(history)
    else:
        prompt = format_template(user_input)

    response = model.generate(prompt, sampling_params)
    display(response)

    if chat_mode:
        history.append({"role": "assistant", "content": response})

Related Pages

• Implementation:OpenRLHF_OpenRLHF_Interactive_Chat