diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index ae3e269ea2..26135a4f82 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -2358,23 +2358,20 @@ static const char * llama_sampler_power_law_name(const struct llama_sampler * /*
     return "power-law";
 }
 
-// compute the adaptive target probability for the current sampling step
-static float llama_sampler_power_law_compute_target(const llama_sampler_power_law * ctx, float decay) {
+// compute the adapted target probability for the current sampling step
+static float llama_sampler_power_law_compute_target(const llama_sampler_power_law * ctx) {
+    const float base_target = ctx->target;
     if (ctx->total_weight == 0.0f) {
-        // if there is no history, just use base target
-        fprintf(stderr, "power-law: compute_target: total_weight == 0.0 (target fixed at %.3f)\n", ctx->target);
-        fflush(stderr);
-        return ctx->target;
+        fprintf(stderr, "power-law: compute_target: total_weight == 0.0 (target fixed at %.3f)\n", base_target);
+        return base_target;
     }
+    float target = 2.0f * base_target - (ctx->weighted_sum / ctx->total_weight);
+    fprintf(stderr, "power-law: compute_target: target = %.3f\n", target);
 
-    // maintain a running weighted sum with exponential decay
-    float new_total_weight = 1.0f + decay * ctx->total_weight;
-    fprintf(stderr, "power-law: compute_target: new_total_weight = %.3f\n", new_total_weight); fflush(stderr);
-    float next_value = ctx->target * new_total_weight - decay * ctx->weighted_sum;
-    fprintf(stderr, "power-law: compute_target: next_value = %.3f\n", next_value); fflush(stderr);
-
-    // clamp to [0.0, 1.0]
-    return std::max(0.0f, std::min(next_value, 1.0f));
+    // clamp result to [0.0, 1.0]
+    target = std::max(0.0f, std::min(target, 1.0f));
+    fprintf(stderr, "power-law: compute_target: target (post-clamp) = %.3f\n", target); fflush(stderr);
+    return target;
 }
 
 static void llama_sampler_power_law_apply(struct llama_sampler * smpl, llama_token_data_array * cur_p) {
@@ -2393,11 +2390,6 @@ static void llama_sampler_power_law_apply(struct llama_sampler * smpl, llama_tok
     const float decay = std::min(ctx->decay, 0.99f);
     fprintf(stderr, "power-law: decay = %.3f\n", decay); fflush(stderr);
 
-    // fixed power law transform parameters
-    const float distribution_width = 0.3f;
-    const float peak_logit_value   = 5.0f;
-    const float tail_heaviness     = 2.0f;
-
     // get the original probabilities
     llama_sampler_softmax_impl(cur_p, false);
 
@@ -2408,21 +2400,22 @@ static void llama_sampler_power_law_apply(struct llama_sampler * smpl, llama_tok
         original_probs.push_back(cur_p->data[i].p);
     }
 
-    float computed_target = llama_sampler_power_law_compute_target(ctx, decay);
+    float computed_target = llama_sampler_power_law_compute_target(ctx);
     fprintf(stderr, "power-law: computed_target = %.3f\n", computed_target); fflush(stderr);
 
     //
     // power law transform
     //
 
+    // transformation constants
+    const float distribution_width = 0.3f;
+    const float peak_logit_value   = 5.0f;
+
+    const float inv_width = 1.0f / distribution_width;
+
     for (size_t i = 0; i < cur_p->size; ++i) {
-        float p = cur_p->data[i].p;
-        fprintf(stderr, "power-law: transform: p = %.3f\n", p); fflush(stderr);
-        float normed_distance = std::abs(p - computed_target) / distribution_width;
-        fprintf(stderr, "power-law: transform: normed_distance = %.3f\n", normed_distance); fflush(stderr);
-        float new_p = peak_logit_value / (1.0f + std::pow(normed_distance, tail_heaviness));
-        fprintf(stderr, "power-law: transform: new_p = %.3f\n", new_p); fflush(stderr);
-        cur_p->data[i].logit = new_p;
+        float dist = (cur_p->data[i].p - computed_target) * inv_width;
+        cur_p->data[i].logit = peak_logit_value / (1.0f + dist * dist);
     }
 
     llama_sampler_softmax_impl(cur_p, false);
@@ -2430,7 +2423,7 @@ static void llama_sampler_power_law_apply(struct llama_sampler * smpl, llama_tok
     // sample from transformed distribution
     const int idx   = llama_sample_dist(cur_p, ctx->rng);
     cur_p->selected = idx;
-    fprintf(stderr, "power-law: selected token %d\n", idx); fflush(stderr);
+    fprintf(stderr, "power-law: selected token at index %d\n", idx); fflush(stderr);
 
     // update running history with the original probability of the selected token
     float original_p  = original_probs[idx];